Predictive analytics for emergency detection and response management

ABSTRACT

Disclosed are systems, methods, and media capable of generating emergency predictions. The systems, methods, and media generate spatiotemporal emergency communication predictions, carry out data augmentation, detect emergency anomalies, optimize emergency resource allocation, or any combination thereof.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of Ser. No. 15/682,440, filed Aug.21, 2017, which claims the benefit of U.S. Application Ser. No.62/516,551, filed Jun. 7, 2017, U.S. Application Ser. No. 62/482,901,filed Apr. 7, 2017, and U.S. Application Ser. No. 62/377,855, filed Aug.22, 2016, the entire contents of which are hereby incorporated byreference.

BACKGROUND

An estimated 240 million 911 phone calls are made each year in the U.S.with some areas receiving a large majority of these emergency calls fromwireless devices. Through the course of a year, a given public safetyanswering point and associated emergency response personnel responsiblefor emergencies within a geographic area will experience a wide range ofcalls on a day-to-day basis. Such fluctuations are universal foremergency response personnel in countries around the world and canresult in substantial understaffing or overstaffing of resources duringspecific time periods. Resources may be misallocated at variouslocations such that the ability of emergency response personnel torespond rapidly to developing emergency situations is impaired.

SUMMARY

The number of emergency communications with public safety answeringpoints, emergency dispatch centers, emergency management systems, andother such emergency response resources is subject to various factorsthat affect the type and frequency of such requests. Influential factorscan include environmental conditions such as weather events (e.g., snow,rain, freezing temperatures, etc.) that may make road conditions moredangerous for motorists. Non-environmental events can also play a role.For example, an annual home game by the local sports team against adivision rival may correlate with increased traffic around the downtownstadium and, in turn, with an increased risk of traffic accidents inthat geographic area. A combination of factors can combine to produceelevated risks for certain types of emergencies. For example, tripledigit temperatures with high humidity during an outdoor sporting eventwith a large audience in attendance may correlate with an elevated riskof heat stroke for athletes and/or attendees.

Because the various factors that influence the risk of an emergency tendto fluctuate over time, the actual number of emergencies or emergencyrequests for a particular geographic area during a particular timeperiod will also vary depending on the environmental conditions and/orevents in that area during that time period. Currently, emergencyresponse personnel are staffed and assigned without the benefit of asystem that accounts for these risk factors. The result is inefficientresource allocation that can lead to inadequate responses to emergenciesby overstretched personnel or emergency resources sitting idle due tooverstaffing.

Moreover, modern digital devices have access to a variety ofinformation, but have been unable to harness that information to detectemergency events. In various cases, a user of a device has an emergencysituation arise but is either unable to request help or is unaware ofthe emergency situation. In these situations, the user's inability torequest assistance can lead to delay or even a complete lack ofemergency response. With the growing availability of user and sensordata from smart devices such as smart phones, IoT devices, etc.,provided herein are systems, methods, and media for obtaining and/orusing affiliate data around an emergency to understand contributingfactors at a micro level (e.g., health indicators) or a macro level(e.g., weather, traffic).

One major advantage of the systems, methods, and media provided hereinis that they provide a means of utilizing historical data on pastemergency calls, environmental conditions, and events to generatespatiotemporal emergency communication predictions. A spatiotemporalprediction typically corresponds to a defined time period and a definedgeographic area. In some embodiments, a user (e.g., a PSAPadministrator) accesses an emergency management system to obtain one ormore spatiotemporal emergency communication predictions. In someembodiments, the user defines a time period and a geographic area. Insome embodiments, the spatiotemporal risk predictions is generated on amacro level (e.g., for a county) and enables the emergency resources forthe county to be allocated ahead of time in preparation for peaks orvalleys in the volume of predicted emergencies or in the location ofwhere those emergencies are likely to occur. Another advantage of someembodiments of the systems, devices and methods described herein is thatthe emergency predictions are generated automatically andrecommendations for reducing emergencies are automatically provided toaffected users (e.g., a mapping program may guide a driver away from anaccident-prone area based on emergency predictions).

Another advantage of the systems, methods, and media provided herein isthe augmentation of unlabeled incoming or recent (e.g., current)emergency communications to predict the nature and/or priority of thecommunications using historical data matched emergency communications.Emergency management systems typically lack access to recent or currentlabeled emergency communication data, including, for example,information regarding the nature or priority of a 911 call. However, thesubject matter disclosed herein enables historical labeled emergencycommunication data obtained from emergency dispatch centers andunlabeled emergency communication data stored by the EMS to be matchedand then used to predict the nature and/or priority of recent emergencycommunications.

Another advantage of the systems, methods, and media provided herein isdetection of one or more emergency anomalies in real-time or nearreal-time. Current or recent emergency communications are monitored todetect any anomalous spatiotemporal clustering compared to historicaland/or predicted communications. Anomalous clusters are optionallyreported or provided to a user such as, for example, an emergencydispatch center administrator. In some embodiments, emergencypredictions are integrated into existing services such as mappingprograms, corporate security and insurance programs.

Another advantage of the systems, methods, and media provided herein isenhancement of emergency resource allocation. Emergency dispatch centersand emergency response resources lack an effective method for optimizingallocation of emergency resources. Spatiotemporal emergencycommunication prediction(s) and resource availability data are used inoptimization algorithms to generate an optimized allocation.

Another advantage of the systems, methods, and media provided herein isauto-detection of emergency (and non-emergency) events. In somescenarios, when a user is unable or unaware of a potential emergencysituation, a device or a group of devices monitors various forms ofinformation such as sensor data to detect emergency or possibleemergency events. For example, systems, methods, and media providedherein may associate certain sensor data such as health indicators froma wearable with environmental data such as temperature and humidity topredict an increased risk for a health emergency. In many instances, thedevice or group of devices then sends emergency alerts and/or requestsfor assistance to a recipient such as, for example, a user, an emergencydispatch enter, operations center, a mapping software, a connecteddevice or healthcare system.

In one aspect, disclosed herein are methods of creating a predictionmodel for generating at least one spatiotemporal emergency prediction,the methods comprising: a) obtaining, by an emergency prediction system(EPS), emergency data comprising emergency type, emergency location, andemergency time for a plurality of emergency communications; b)generating, by the emergency prediction system, at least one predictionmodel for making at least one spatiotemporal emergency prediction usingthe emergency data; and c) using, by the emergency prediction system,the at least one prediction model to generate a spatiotemporal emergencyprediction corresponding to a defined emergency type, a definedgeographic area, and a defined time period. In some embodiments, the atleast one prediction model is generated using a point cloud comprisingcurrent emergency data. In further embodiments, the point cloud includescurrent proprietary emergency data. In some embodiments, the at leastone prediction model is generated using a point cloud comprising aproprietary data stream. In some embodiments, generating thespatiotemporal emergency prediction comprises making and aggregatingpredictions corresponding to subsets of the defined time period. In someembodiments, generating the spatiotemporal emergency predictioncomprises making and aggregating predictions corresponding to subsets ofthe defined geographic area. In some embodiments, the spatiotemporalemergency prediction is used for emergency resource allocation,anomalous cluster detection, spatiotemporal emergency predictionvisualization, or any combination thereof. In some embodiments, the atleast one spatiotemporal emergency prediction comprises a predictednumber of emergency communications. In some embodiments, the at leastone spatiotemporal emergency prediction comprises a predicted emergencycommunication density. In some embodiments, the at least onespatiotemporal emergency prediction comprises a set of predicted kerneldensity estimates. In some embodiments, the at least one spatiotemporalemergency prediction comprises predicted response time. In someembodiments, the at least one spatiotemporal emergency predictioncomprises emergency priority. In some embodiments, the method furthercomprises providing, by the emergency prediction system, thespatiotemporal emergency prediction to an emergency dispatch centerserving the defined geographic area. In further embodiments, providingthe spatiotemporal emergency prediction comprises displaying a set ofpredicted kernel density estimates on a digital map. In yet furtherembodiments, the digital map shows at least a portion of the definedgeographic area. In further embodiments, the emergency prediction systemprovides the spatiotemporal emergency prediction in response to arequest from the emergency dispatch center, an operations center,mapping software, or a connected device. In further embodiments, theemergency prediction system provides the spatiotemporal emergencyprediction to an emergency dispatch center autonomously. In someembodiments, the at least one prediction model is updated with newemergency data. In some embodiments, the at least one prediction modelis trained using labeled call data, unlabeled call data, augmented calldata, or any combination thereof. In some embodiments, the at least oneprediction model is assessed for prediction accuracy. In furtherembodiments, prediction accuracy is determined by comparing at least onehistorical spatiotemporal emergency prediction to an actual number ofemergency communications. In some embodiments, the at least oneprediction model is re-created or re-trained. In some embodiments, theat least one prediction model is re-trained at least once a week. Insome embodiments, the emergency data comprises labeled call data. Insome embodiments, the emergency data comprises unlabeled call data. Insome embodiments, the emergency data comprises augmented call data. Infurther embodiments, the augmented call data is obtained by matchingunlabeled call data with labeled call data. In yet further embodiments,matching is based on call identity, emergency time, emergency location,call duration, or any combination thereof. In yet further embodiments,the unlabeled call data and labeled call data are merged to formaugmented call data. In some embodiments, the emergency data compriseshistorical emergency data. In some embodiments, the emergency datacomprises current emergency data. In some embodiments, the emergencydata comprises public safety answering point (PSAP) call data. In someembodiments, the emergency data comprises EPS call data. In someembodiments, the emergency data is augmented with environment dataassociated with the plurality of emergency communications based onemergency time and emergency location. In further embodiments, theenvironment data comprises weather information, traffic information,road condition information, or any combination thereof. In someembodiments, the emergency data comprises event data. In furtherembodiments, the event data comprises information on a concert, sportingevent, political demonstration, festival, performance, riot, protest,parade, convention, political campaign event, or any combinationthereof. In some embodiments, emergency type is selected from fireemergency, medical emergency, car accident, police emergency, andnatural disaster. In some embodiments, the emergency time is a time whenan emergency communication is initiated or received or a duration of theemergency communication. In some embodiments, the defined time periodcomprises a time block. In further embodiments, the time block is atleast about 1 hour. In further embodiments, the time block is at leastabout 1 day. In further embodiments, the time block is at least about 1week. In further embodiments, the time block is at least one day duringa week. In further embodiments, the time block is at least one dayduring a weekend. In some embodiments, the defined geographic areacomprises an area block. In further embodiments, the area block is anarea of about 100 square meters. In further embodiments, the area blockis an area of about 1000 square meters. In further embodiments, the areablock is an area of about 5 square kilometers. In further embodiments,the area block is an area of about 10 square kilometers. In someembodiments, the defined geographic area corresponds to a governmentdefined area. In some embodiments, the defined geographic areacorresponds to a government defined area. In some embodiments, thedefined geographic area is a PSAP jurisdiction, a zip code, a censustract, a city, a county, or any combination thereof. In someembodiments, the at least one prediction model comprises a plurality ofprediction models corresponding to a plurality of defined geographicareas, wherein each of the plurality of prediction models corresponds toa defined geographic area. In some embodiments, the at least oneprediction model comprises a plurality of prediction modelscorresponding to a plurality of defined emergency types, wherein each ofthe plurality of prediction models corresponds to a defined emergencytype. In some embodiments, a new spatiotemporal emergency prediction isgenerated periodically on a sliding window over time.

In another aspect, disclosed herein are methods of creating a predictionmodel for generating at least one spatiotemporal emergency prediction,the methods comprising: a) obtaining, by an emergency prediction system(EPS), emergency data for a plurality of emergencies, the emergency datacomprising emergency location and emergency time; b) generating, by theemergency prediction system, at least one prediction model for making atleast one spatiotemporal emergency prediction using the emergency data;and c) using, by the emergency prediction system, the at least oneprediction model to generate a spatiotemporal emergency predictioncorresponding to a defined geographic area and a defined time period.

In another aspect, disclosed herein are computer-implemented methods foraugmenting unlabeled emergency data, comprising: a) obtaining, by anemergency prediction system, unlabeled emergency data; b) obtaining, bythe emergency prediction system, historical labeled emergency dataoriginating from at least one emergency dispatch center, said historicallabeled emergency data comprising emergency type and emergency priority;c) matching, by the emergency prediction system, at least a subset ofthe unlabeled emergency data with at least a subset of the historicallabeled emergency data; and d) merging, by the emergency predictionsystem, the at least a subset of the unlabeled emergency data with theat least a subset of the historical labeled emergency data to generatematched emergency data. In some embodiments, the method furthercomprises providing, by the emergency prediction system, a responseprediction model. In further embodiments, the response prediction modelis a regression model. In further embodiments, the method furthercomprises using, by the emergency prediction system, the responseprediction model to generate at least one estimated response time for atleast one emergency communication. In yet further embodiments, the atleast one estimated response time is generated in real-time for at leastone emergency communication. In yet further embodiments, the at leastone estimated response time comprises an exact time. In yet furtherembodiments, the at least one estimated response time comprises a timerange. In yet further embodiments, the method further comprisesproviding, by the emergency prediction system, the at least oneestimated response time to at least one emergency dispatch center. Inyet further embodiments, the method further comprises providing, by theemergency prediction system, the at least one estimated response time toat least one communication device initiating the at least one emergencycommunication. In some embodiments, the method further comprisesobtaining, by the emergency prediction system, a prediction algorithmfor predicting labels for an incoming data stream of unlabeled emergencydata. In further embodiments, the prediction algorithm is trained usingmatched emergency data. In further embodiments, the prediction algorithmis a multi-class classifier for predicting at least one of emergencytype and emergency priority for unlabeled emergency data. In yet furtherembodiments, the at least one of emergency type and emergency prioritypredicted by the multi-class classifier is incorporated into theunlabeled emergency data, thereby converting the unlabeled emergencydata to augmented emergency data. In yet further embodiments, the methodfurther comprises using, by the emergency prediction system, themulti-class classifier to generate at least one emergency typeprediction for current unlabeled emergency data. In yet furtherembodiments, the method further comprises using, by the emergencyprediction system, the multi-class classifier to generate at least oneemergency priority prediction for current unlabeled emergency data. Infurther embodiments, the prediction algorithm provides a predictionprobability. In further embodiments, the prediction algorithm isassessed for prediction accuracy. In yet further embodiments, predictionaccuracy is determined by comparing predicted labels to actual labels.In further embodiments, the prediction algorithm is re-trained. Infurther embodiments, the prediction algorithm is re-trained at leastonce a week. In further embodiments, the method further comprisesproviding, by the emergency prediction system, the augmented emergencydata to at least one emergency dispatch center. In some embodiments, theemergency priority is priority call or non-priority call. In someembodiments, the emergency priority is a priority level assigned by anemergency dispatch center. In some embodiments, emergency type isselected from fire emergency, medical emergency, car accident, policeemergency, and natural disaster.

In another aspect, disclosed herein are computer-implemented methods forpredicting labels for an emergency communication data stream, themethods comprising: a) obtaining, by an emergency prediction system,unlabeled emergency data; b) obtaining, by the emergency predictionsystem, historical labeled emergency data originating from at least oneemergency dispatch center; c) matching, by the emergency predictionsystem, at least a subset of the unlabeled emergency data with at leasta subset of the historical labeled emergency data to generate matchedemergency data; d) training, by the emergency prediction system, aprediction algorithm using the matched emergency data; and e) using, bythe emergency prediction system, the prediction algorithm to predictlabels for an incoming data stream of unlabeled emergency data. In someembodiments, the prediction algorithm is a multi-class classifier forpredicting at least one of emergency type and emergency priority forunlabeled emergency communication data. In further embodiments, theemergency priority is priority call or non-priority call. In furtherembodiments, the emergency priority is a priority level assigned by anemergency dispatch center. In further embodiments, emergency type isselected from fire emergency, medical emergency, car accident, policeemergency, and natural disaster. In further embodiments, the at leastone of emergency type and emergency priority predicted by themulti-class classifier is incorporated into the incoming data stream ofunlabeled emergency data to generate augmented emergency data. In yetfurther embodiments, the method comprises providing, by the emergencyprediction system, the augmented emergency data to at least oneemergency dispatch center. In further embodiments, the method comprisesusing, by the emergency prediction system, the multi-class classifier togenerate at least one emergency type prediction for an incoming datastream of unlabeled emergency data. In further embodiments, the methodcomprises using, by the emergency prediction system, the multi-classclassifier to generate at least one emergency priority prediction for anincoming data stream of unlabeled emergency data. In some embodiments,the prediction algorithm provides a prediction probability. In someembodiments, the prediction algorithm is assessed for predictionaccuracy. In further embodiments, prediction accuracy is determined bycomparing predicted labels to actual labels. In some embodiments, theprediction algorithm is re-trained. In some embodiments, the predictionalgorithm is re-trained at least once a week.

In another aspect, disclosed herein are methods for detecting anemergency anomaly, the methods comprising: a) obtaining, by an emergencyprediction system, emergency data for current or ongoing emergencycommunications, said emergency data comprising emergency time andemergency location; b) providing, by the emergency prediction system, anemergency anomaly detection algorithm for monitoring the emergencycommunications to identify the emergency anomaly; and c) executing, bythe emergency prediction system, the emergency anomaly detectionalgorithm to identify the emergency anomaly based on the emergency data,said emergency anomaly comprising a cluster of emergency communications.In some embodiments, the method comprises using, by the emergencyprediction system, a multi-class classifier to predict at least one ofemergency type and emergency priority for the cluster of emergencycommunications. In some embodiments, the emergency data compriseslabeled call data. In some embodiments, the emergency data comprisesunlabeled call data. In some embodiments, the emergency data comprisesaugmented call data. In some embodiments, the emergency anomaly isidentified in real-time. In some embodiments, the emergency anomalydetection is identified in near real-time. In some embodiments, theemergency anomaly detection algorithm uses a cluster detection model toidentify the emergency anomaly. In some embodiments, the emergencyanomaly detection algorithm executes upon each incoming new emergencycommunication. In some embodiments, the emergency anomaly detectionalgorithm executes periodically on a discrete time interval. In someembodiments, the emergency anomaly detection algorithm executes uponreceiving instruction from a user, administrator, an emergencyprediction system, an emergency management system, or an emergencydispatch center. In some embodiments, the cluster comprises emergencycommunications that correspond to a defined geographic area and adefined time period. In further embodiments, the defined time periodcomprises a time block. In yet further embodiments, the time block is atleast about 5 minutes. In yet further embodiments, the time block is atleast about 10 minutes. In yet further embodiments, the time block is atleast about 30 minutes. In yet further embodiments, the defined timeperiod is determined based on population density and call volume. Infurther embodiments, the defined geographic area comprises an areablock. In yet further embodiments, the area block is an area of about100 square meters. In yet further embodiments, the area block is an areaof about 1000 square meters. In yet further embodiments, the area blockis an area of about 5 square kilometers. In yet further embodiments, thearea block is an area of about 10 square kilometers. In yet furtherembodiments, the defined geographic area is determined based onpopulation density and call volume. In yet further embodiments, thedefined geographic area corresponds to a government defined area. In yetfurther embodiments, the defined geographic area corresponds to agovernment defined area. In yet further embodiments, the definedgeographic area is a PSAP jurisdiction, a zip code, a census tract, acity, a county, or any combination thereof. In yet further embodiments,the emergency data further comprises emergency type. In still yetfurther embodiments, the cluster comprises emergency communications thatcorrespond to a defined geographic area, a defined time period, and adefined emergency type. In some embodiments, the emergency data isaugmented with environment data. In some embodiments, the emergency datais augmented with event data. In some embodiments, the emergency anomalydetection algorithm identifies the emergency anomaly as being associatedwith a natural disaster or a man-made disaster. In some embodiments, theemergency anomaly detection algorithm identifies the emergency anomalyas being associated with an earthquake, landslide, tsunami, volcanicactivity, wildfire, large-scale fire, cyclone, tornado, hurricane,epidemic, extreme temperature, industrial accident, chemical spill,nuclear accident, terrorist attack, or large-scale transport accident.In some embodiments, the method comprises providing, by the emergencyprediction system, the emergency anomaly to an emergency dispatchcenter. In further embodiments, the emergency anomaly is provided as thecluster of emergency communications. In yet further embodiments, theemergency prediction system further provides information about thecluster comprising a center, a radius, a start time, an end time,p-value, number of calls, expected number of calls, or any combinationthereof. In further embodiments, providing the emergency anomalycomprises displaying the cluster of emergency communications on adigital map. In yet further embodiments, the emergency prediction systemprovides the emergency anomaly in response to a request from theemergency dispatch center. In yet further embodiments, the emergencyprediction system provides the emergency anomaly autonomously. In someembodiments, the method comprises locating, by the emergency predictionsystem, at least one subject located within the emergency anomaly basedon subject mobility data. In further embodiments, the method comprisessending, by the emergency prediction system, a notification of theemergency anomaly to the at least one subject located within theemergency anomaly. In some embodiments, the method comprises locating,by the emergency prediction system, at least one subject located withinthe emergency anomaly based on a defined geographic area and a definedtime period of the emergency anomaly. In further embodiments, the methodcomprises sending, by the emergency prediction system, a notification ofthe emergency anomaly to the at least one subject located within theemergency anomaly. In some embodiments, emergency anomaly detection iscarried out for a location of a first member device belonging to a groupof devices authorized to share data. In further embodiments, anotification of a detected emergency anomaly is sent to a second memberdevice in the group of devices. In some embodiments, emergency anomalydetection is carried out for a first member device belonging to a groupof devices based on information provided by a group of devices. Infurther embodiments, a proxy call is initiated on behalf of the firstmember device when an emergency anomaly is detected for the memberdevice. In yet further embodiments, the proxy call is an emergency callto at least one of an emergency management system and an emergencydispatch center. In further embodiments, a proxy call is initiated onbehalf of the first member device when an emergency anomaly is detectedat a location of the first member device. In further embodiments, aproxy call is initiated on behalf of the first member device by a secondmember device in the group of devices when an emergency anomaly isdetected for the first member device. In yet further embodiments, alocation of the first member device is provided to a recipient of theproxy call. In yet further embodiments, the emergency data is providedto a recipient of the proxy call. In some embodiments, the emergencydata is obtained from a group of devices comprising member devicesauthorized to share data. In some embodiments, the emergency predictionsystem executes the emergency anomaly detection algorithm in response toreceiving a request to detect an emergency anomaly from a communicationsdevice. In some embodiments, the emergency prediction system executesthe emergency anomaly detection algorithm in response to receiving arequest to detect an emergency anomaly from a member device in a groupof devices authorized to share data.

In another aspect, disclosed herein are methods for optimizing emergencyresource allocation using emergency data, comprising: a) obtaining, byan emergency resource management system, at least one spatiotemporalemergency prediction; b) obtaining, by the emergency resource managementsystem, at least one estimated response time prediction corresponding tothe at least one spatiotemporal emergency prediction; c) obtaining, bythe emergency resource management system, local emergency resourceallocation data; and d) using, by the emergency resource managementsystem, an allocation algorithm to generate a recommendation for optimalallocation of local emergency resources based on the at least onespatiotemporal emergency prediction, the at least one estimated responsetime prediction, and the local emergency resource allocation data. Insome embodiments, the allocation algorithm comprises a greedy allocationalgorithm. In some embodiments, the optimal allocation minimizes apredicted overall emergency response time. In some embodiments, theoptimal allocation minimizes a number of emergency communications havingan emergency response time exceeding a threshold time. In furtherembodiments, the threshold time is no more than about 10 minutes. Infurther embodiments, the threshold time is no more than about 20minutes. In further embodiments, the local emergency resources compriseemergency response vehicle, emergency response personnel, emergencyresponse equipment, emergency response base, or any combination thereof.In further embodiments, the local emergency resource allocation datacomprises number or amount of local emergency resources, location oflocal emergency resources, restraints on allocation of local emergencyresources, restraints on dispatch of local emergency resources, or anycombination thereof. In further embodiments, the optimal allocation isbased on one emergency type. In further embodiments, the optimalallocation is based on multiple emergency types. In further embodiments,the optimal allocation reduces overall predicted response time for thespatiotemporal emergency prediction by at least 10%. In furtherembodiments, the optimal allocation enables short-term dynamicreallocation of the local emergency resources. In further embodiments,the optimal allocation enables long-term allocation of the localemergency resources. In further embodiments, the optimal allocation ispredicted for a defined time period. In yet further embodiments, thedefined time period comprises a time block. In still yet furtherembodiments, the time block is at least about 1 hour. In still yetfurther embodiments, the time block is at least about 1 day. In stillyet further embodiments, the time block is at least about 1 week. Instill yet further embodiments, the time block is at least one day duringa week. In still yet further embodiments, the time block is at least oneday during a weekend. In still yet further embodiments, the time blockis at least a day in the future. In still yet further embodiments, thetime block is at least a week in the future. In some embodiments, theoptimal allocation is stored on a database. In some embodiments, themethod comprises providing, by the emergency resource management system,the optimal allocation to an emergency dispatch center. In furtherembodiments, providing the optimal allocation comprises displaying thelocal emergency resources according to the optimal allocation on adigital map. In further embodiments, the emergency resource managementsystem provides the optimal allocation in response to a request from theemergency dispatch center. In further embodiments, the emergencyresource management system provides the optimal allocation to anemergency dispatch center autonomously. In some embodiments, the atleast one prediction model is updated with new emergency data. In someembodiments, the method comprises providing, by the emergency resourcemanagement system, a simulation platform for an administrator tosimulate a local emergency resource allocation. In some embodiments, anestimated response time is calculated for the local emergency resourceallocation provided by the simulation platform.

In another aspect, disclosed herein are emergency prediction systems(EPS) comprising at least one processor, an operating system configuredto perform executable instructions, a memory, and a computer programincluding instructions executable by the at least one processor tocreate an application comprising: a) a software module obtainingemergency data comprising emergency type, emergency location, andemergency time for a plurality of emergency communications; b) asoftware module generating at least one prediction model for making atleast one spatiotemporal emergency prediction using the emergency data;and c) a software module using the at least one prediction model togenerate a spatiotemporal emergency prediction corresponding to adefined emergency type, a defined geographic area, and a defined timeperiod. In some embodiments, the at least one prediction model isgenerated using a point cloud comprising current emergency data. Infurther embodiments, the point cloud includes current proprietaryemergency data. In some embodiments, the at least one prediction modelis generated using a point cloud comprising a proprietary data stream.In some embodiments, generating the spatiotemporal emergency predictioncomprises making and aggregating predictions corresponding to subsets ofthe defined time period. In some embodiments, generating thespatiotemporal emergency prediction comprises making and aggregatingpredictions corresponding to subsets of the defined geographic area. Insome embodiments, the spatiotemporal emergency prediction is used foremergency resource allocation, anomalous cluster detection,spatiotemporal emergency prediction visualization, or any combinationthereof. In some embodiments, the at least one spatiotemporal emergencyprediction comprises a predicted number of emergency communications. Insome embodiments, the at least one spatiotemporal emergency predictioncomprises a predicted emergency communication density. In someembodiments, the at least one spatiotemporal emergency predictioncomprises a set of predicted kernel density estimates. In someembodiments, the at least one spatiotemporal emergency predictioncomprises predicted response time. In some embodiments, the at least onespatiotemporal emergency prediction comprises emergency priority. Insome embodiments, the emergency prediction system provides thespatiotemporal emergency prediction to an emergency dispatch centerserving the defined geographic area. In further embodiments, providingthe spatiotemporal emergency prediction comprises displaying a set ofpredicted kernel density estimates on a digital map. In yet furtherembodiments, the digital map shows at least a portion of the definedgeographic area. In further embodiments, the emergency prediction systemprovides the spatiotemporal emergency prediction in response to arequest from the emergency dispatch center, an operations center,mapping software, or a connected device. In further embodiments, theemergency prediction system provides the spatiotemporal emergencyprediction to an emergency dispatch center autonomously. In someembodiments, the at least one prediction model is updated with newemergency data. In some embodiments, the at least one prediction modelis trained using labeled call data, unlabeled call data, augmented calldata, or any combination thereof. In some embodiments, the at least oneprediction model is assessed for prediction accuracy. In furtherembodiments, prediction accuracy is determined by comparing at least onehistorical spatiotemporal emergency prediction to an actual number ofemergency communications. In some embodiments, the at least oneprediction model is re-created or re-trained. In some embodiments, theat least one prediction model is re-trained at least once a week. Insome embodiments, the emergency data comprises labeled call data. Insome embodiments, the emergency data comprises unlabeled call data. Insome embodiments, the emergency data comprises augmented call data. Infurther embodiments, the augmented call data is obtained by matchingunlabeled call data with labeled call data. In yet further embodiments,matching is based on call identity, emergency time, emergency location,call duration, or any combination thereof. In yet further embodiments,the unlabeled call data and labeled call data are merged to formaugmented call data. In some embodiments, the emergency data compriseshistorical emergency data. In some embodiments, the emergency datacomprises current emergency data. In some embodiments, the emergencydata comprises public safety answering point (PSAP) call data. In someembodiments, the emergency data comprises EPS call data. In someembodiments, the emergency data is augmented with environment dataassociated with the plurality of emergency communications based onemergency time and emergency location. In further embodiments, theenvironment data comprises weather information, traffic information,road condition information, or any combination thereof. In someembodiments, the emergency data comprises event data. In furtherembodiments, the event data comprises information on a concert, sportingevent, political demonstration, festival, performance, riot, protest,parade, convention, political campaign event, or any combinationthereof. In some embodiments, emergency type is selected from fireemergency, medical emergency, car accident, police emergency, andnatural disaster. In some embodiments, the emergency time is a time whenan emergency communication is initiated or received or a duration of theemergency communication. In some embodiments, the defined time periodcomprises a time block. In further embodiments, the time block is atleast about 1 hour. In further embodiments, the time block is at leastabout 1 day. In further embodiments, the time block is at least about 1week. In further embodiments, the time block is at least one day duringa week. In further embodiments, the time block is at least one dayduring a weekend. In some embodiments, the defined geographic areacomprises an area block. In further embodiments, the area block is anarea of about 100 square meters. In further embodiments, the area blockis an area of about 1000 square meters. In further embodiments, the areablock is an area of about 5 square kilometers. In further embodiments,the area block is an area of about 10 square kilometers. In someembodiments, the defined geographic area corresponds to a governmentdefined area. In some embodiments, the defined geographic areacorresponds to a government defined area. In some embodiments, thedefined geographic area is a PSAP jurisdiction, a zip code, a censustract, a city, a county, or any combination thereof. In someembodiments, the at least one prediction model comprises a plurality ofprediction models corresponding to a plurality of defined geographicareas, wherein each of the plurality of prediction models corresponds toa defined geographic area. In some embodiments, the at least oneprediction model comprises a plurality of prediction modelscorresponding to a plurality of defined emergency types, wherein each ofthe plurality of prediction models corresponds to a defined emergencytype. In some embodiments, a new spatiotemporal emergency prediction isgenerated periodically on a sliding window over time.

In another aspect, disclosed herein are emergency prediction systems(EPS) comprising at least one processor, an operating system configuredto perform executable instructions, a memory, and a computer programincluding instructions executable by the at least one processor tocreate an application comprising: a) a software module obtainingemergency data for a plurality of emergencies, the emergency datacomprising emergency location and emergency time; b) a software modulegenerating at least one prediction model for making at least onespatiotemporal emergency prediction using the emergency data; and c) asoftware module using the at least one prediction model to generate aspatiotemporal emergency prediction corresponding to a definedgeographic area and a defined time period.

In another aspect, disclosed herein are non-transitory computer-readablestorage media encoded with a computer program including instructionsexecutable by at least one processor to create an emergency predictionapplication comprising: a) a software module obtaining emergency datacomprising emergency type, emergency location, and emergency time for aplurality of emergency communications; b) a software module generatingat least one prediction model for making at least one spatiotemporalemergency prediction using the emergency data; and c) a software moduleusing the at least one prediction model to generate a spatiotemporalemergency prediction corresponding to a defined emergency type, adefined geographic area, and a defined time period. In some embodiments,the at least one prediction model is generated using a point cloudcomprising current emergency data. In further embodiments, the pointcloud includes current proprietary emergency data. In some embodiments,the at least one prediction model is generated using a point cloudcomprising a proprietary data stream. In some embodiments, generatingthe spatiotemporal emergency prediction comprises making and aggregatingpredictions corresponding to subsets of the defined time period. In someembodiments, generating the spatiotemporal emergency predictioncomprises making and aggregating predictions corresponding to subsets ofthe defined geographic area. In some embodiments, the spatiotemporalemergency prediction is used for emergency resource allocation,anomalous cluster detection, spatiotemporal emergency predictionvisualization, or any combination thereof. In some embodiments, the atleast one spatiotemporal emergency prediction comprises a predictednumber of emergency communications. In some embodiments, the at leastone spatiotemporal emergency prediction comprises a predicted emergencycommunication density. In some embodiments, the at least onespatiotemporal emergency prediction comprises a set of predicted kerneldensity estimates. In some embodiments, the at least one spatiotemporalemergency prediction comprises predicted response time. In someembodiments, the at least one spatiotemporal emergency predictioncomprises emergency priority. In some embodiments, the emergencyprediction application provides the spatiotemporal emergency predictionto an emergency dispatch center serving the defined geographic area. Infurther embodiments, providing the spatiotemporal emergency predictioncomprises displaying a set of predicted kernel density estimates on adigital map. In yet further embodiments, the digital map shows at leasta portion of the defined geographic area. In further embodiments, theemergency prediction system provides the spatiotemporal emergencyprediction in response to a request from the emergency dispatch center,an operations center, mapping software, or a connected device. Infurther embodiments, the emergency prediction system provides thespatiotemporal emergency prediction to an emergency dispatch centerautonomously. In some embodiments, the at least one prediction model isupdated with new emergency data. In some embodiments, the at least oneprediction model is trained using labeled call data, unlabeled calldata, augmented call data, or any combination thereof. In someembodiments, the at least one prediction model is assessed forprediction accuracy. In further embodiments, prediction accuracy isdetermined by comparing at least one historical spatiotemporal emergencyprediction to an actual number of emergency communications. In someembodiments, the at least one prediction model is re-created orre-trained. In some embodiments, the at least one prediction model isre-trained at least once a week. In some embodiments, the emergency datacomprises labeled call data. In some embodiments, the emergency datacomprises unlabeled call data. In some embodiments, the emergency datacomprises augmented call data. In further embodiments, the augmentedcall data is obtained by matching unlabeled call data with labeled calldata. In yet further embodiments, matching is based on call identity,emergency time, emergency location, call duration, or any combinationthereof. In yet further embodiments, the unlabeled call data and labeledcall data are merged to form augmented call data. In some embodiments,the emergency data comprises historical emergency data. In someembodiments, the emergency data comprises current emergency data. Insome embodiments, the emergency data comprises public safety answeringpoint (PSAP) call data. In some embodiments, the emergency datacomprises EPS call data. In some embodiments, the emergency data isaugmented with environment data associated with the plurality ofemergency communications based on emergency time and emergency location.In further embodiments, the environment data comprises weatherinformation, traffic information, road condition information, or anycombination thereof. In some embodiments, the emergency data comprisesevent data. In further embodiments, the event data comprises informationon a concert, sporting event, political demonstration, festival,performance, riot, protest, parade, convention, political campaignevent, or any combination thereof. In some embodiments, emergency typeis selected from fire emergency, medical emergency, car accident, policeemergency, and natural disaster. In some embodiments, the emergency timeis a time when an emergency communication is initiated or received or aduration of the emergency communication. In some embodiments, thedefined time period comprises a time block. In further embodiments, thetime block is at least about 1 hour. In further embodiments, the timeblock is at least about 1 day. In further embodiments, the time block isat least about 1 week. In further embodiments, the time block is atleast one day during a week. In further embodiments, the time block isat least one day during a weekend. In some embodiments, the definedgeographic area comprises an area block. In further embodiments, thearea block is an area of about 100 square meters. In furtherembodiments, the area block is an area of about 1000 square meters. Infurther embodiments, the area block is an area of about 5 squarekilometers. In further embodiments, the area block is an area of about10 square kilometers. In some embodiments, the defined geographic areacorresponds to a government defined area. In some embodiments, thedefined geographic area corresponds to a government defined area. Insome embodiments, the defined geographic area is a PSAP jurisdiction, azip code, a census tract, a city, a county, or any combination thereof.In some embodiments, the at least one prediction model comprises aplurality of prediction models corresponding to a plurality of definedgeographic areas, wherein each of the plurality of prediction modelscorresponds to a defined geographic area. In some embodiments, the atleast one prediction model comprises a plurality of prediction modelscorresponding to a plurality of defined emergency types, wherein each ofthe plurality of prediction models corresponds to a defined emergencytype. In some embodiments, a new spatiotemporal emergency prediction isgenerated periodically on a sliding window over time.

In another aspect, disclosed herein are non-transitory computer-readablestorage media encoded with a computer program including instructionsexecutable by at least one processor to create an emergency predictionapplication comprising: a) a software module obtaining emergency datafor a plurality of emergencies, the emergency data comprising emergencylocation and emergency time; b) a software module generating at leastone prediction model for making at least one spatiotemporal emergencyprediction using the emergency data; and c) a software module using theat least one prediction model to generate a spatiotemporal emergencyprediction corresponding to a defined geographic area and a defined timeperiod.

In another aspect, disclosed herein are emergency prediction systemscomprising: at least one processor, an operating system configured toperform executable instructions, a memory, and a computer programincluding instructions executable by the at least one processor tocreate an application comprising: a) a software module obtainingunlabeled emergency data; b) a software module obtaining historicallabeled emergency data originating from at least one emergency dispatchcenter, said historical labeled emergency data comprising emergency typeand emergency priority; c) a software module matching at least a subsetof the unlabeled emergency data with at least a subset of the historicallabeled emergency data; and d) a software module merging the at least asubset of the unlabeled emergency data with the at least a subset of thehistorical labeled emergency data to generate matched emergency data. Insome embodiments, the emergency prediction system comprises a softwaremodule providing a response prediction model. In further embodiments,the response prediction model is a regression model. In furtherembodiments, the emergency prediction system uses the responseprediction model to generate at least one estimated response time for atleast one emergency communication. In yet further embodiments, the atleast one estimated response time is generated in real-time for at leastone emergency communication. In yet further embodiments, the at leastone estimated response time comprises an exact time. In yet furtherembodiments, the at least one estimated response time comprises a timerange. In yet further embodiments, the emergency prediction systemcomprises a software module providing the at least one estimatedresponse time to at least one emergency dispatch center. In yet furtherembodiments, the emergency prediction system comprises a software moduleproviding the at least one estimated response time to at least onecommunication device initiating the at least one emergencycommunication. In some embodiments, the emergency prediction systemcomprises a software module providing a prediction algorithm forpredicting labels for an incoming data stream of unlabeled emergencydata. In further embodiments, the prediction algorithm is trained usingmatched emergency data. In further embodiments, the prediction algorithmis a multi-class classifier for predicting at least one of emergencytype and emergency priority for unlabeled emergency data. In yet furtherembodiments, the at least one of emergency type and emergency prioritypredicted by the multi-class classifier is incorporated into theunlabeled emergency data, thereby converting the unlabeled emergencydata to augmented emergency data. In yet further embodiments, theemergency prediction system uses the multi-class classifier to generateat least one emergency type prediction for current unlabeled emergencydata. In yet further embodiments, the emergency prediction system usesthe multi-class classifier to generate at least one emergency priorityprediction for current unlabeled emergency data. In further embodiments,the prediction algorithm provides a prediction probability. In furtherembodiments, the prediction algorithm is assessed for predictionaccuracy. In yet further embodiments, prediction accuracy is determinedby comparing predicted labels to actual labels. In further embodiments,the prediction algorithm is re-trained. In further embodiments, theprediction algorithm is re-trained at least once a week. In furtherembodiments, the emergency prediction system comprises providing atleast a subset of the augmented emergency data to at least one emergencydispatch center. In some embodiments, the emergency priority is prioritycall or non-priority call. In some embodiments, the emergency priorityis a priority level assigned by an emergency dispatch center. In someembodiments, emergency type is selected from fire emergency, medicalemergency, car accident, police emergency, and natural disaster.

In another aspect, disclosed herein are non-transitory computer-readablestorage media encoded with a computer program including instructionsexecutable by at least one processor to create an emergency predictionapplication comprising: a) a software module obtaining unlabeledemergency data; b) a software module obtaining historical labeledemergency data originating from at least one emergency dispatch center,said historical labeled emergency data comprising emergency type andemergency priority; c) a software module matching at least a subset ofthe unlabeled emergency data with at least a subset of the historicallabeled emergency data; and d) a software module merging the at least asubset of the unlabeled emergency data with the at least a subset of thehistorical labeled emergency data to generate matched emergency data. Insome embodiments, the emergency prediction application comprises asoftware module providing a response prediction model. In furtherembodiments, the response prediction model is a regression model. Infurther embodiments, the emergency prediction application uses theresponse prediction model to generate at least one estimated responsetime for at least one emergency communication. In yet furtherembodiments, the at least one estimated response time is generated inreal-time for at least one emergency communication. In yet furtherembodiments, the at least one estimated response time comprises an exacttime. In yet further embodiments, the at least one estimated responsetime comprises a time range. In yet further embodiments, the emergencyprediction application comprises a software module providing the atleast one estimated response time to at least one emergency dispatchcenter. In yet further embodiments, the emergency prediction applicationcomprises a software module providing the at least one estimatedresponse time to at least one communication device initiating the atleast one emergency communication. In some embodiments, the emergencyprediction application comprises a software module providing aprediction algorithm for predicting labels for an incoming data streamof unlabeled emergency data. In further embodiments, the predictionalgorithm is trained using matched emergency data. In furtherembodiments, the prediction algorithm is a multi-class classifier forpredicting at least one of emergency type and emergency priority forunlabeled emergency data. In yet further embodiments, the at least oneof emergency type and emergency priority predicted by the multi-classclassifier is incorporated into the unlabeled emergency data, therebyconverting the unlabeled emergency data to augmented emergency data. Inyet further embodiments, the emergency prediction application uses themulti-class classifier to generate at least one emergency typeprediction for current unlabeled emergency data. In yet furtherembodiments, the emergency prediction application uses the multi-classclassifier to generate at least one emergency priority prediction forcurrent unlabeled emergency data. In further embodiments, the predictionalgorithm provides a prediction probability. In further embodiments, theprediction algorithm is assessed for prediction accuracy. In yet furtherembodiments, prediction accuracy is determined by comparing predictedlabels to actual labels. In further embodiments, the predictionalgorithm is re-trained. In further embodiments, the predictionalgorithm is re-trained at least once a week. In further embodiments,the emergency prediction application comprises providing at least asubset of the augmented emergency data to at least one emergencydispatch center. In some embodiments, the emergency priority is prioritycall or non-priority call. In some embodiments, the emergency priorityis a priority level assigned by an emergency dispatch center. In someembodiments, emergency type is selected from fire emergency, medicalemergency, car accident, police emergency, and natural disaster.

In another aspect, disclosed herein are emergency anomaly predictionsystems comprising at least one processor, an operating systemconfigured to perform executable instructions, a memory, and a computerprogram including instructions executable by the at least one processorto create an application comprising: a) a software module obtainingemergency data for current or ongoing emergency communications, saidemergency data comprising emergency time and emergency location; b) asoftware module obtaining an emergency anomaly detection algorithm formonitoring the emergency communications to identify the emergencyanomaly; and c) a software module executing the emergency anomalydetection algorithm to identify the emergency anomaly based on theemergency data, said emergency anomaly comprising a cluster of emergencycommunications. In some embodiments, the emergency prediction systemuses a multi-class classifier to predict at least one of emergency typeand emergency priority for the cluster of emergency communications. Insome embodiments, the emergency data comprises labeled call data. Insome embodiments, the emergency data comprises unlabeled call data. Insome embodiments, the emergency data comprises augmented call data. Insome embodiments, the emergency anomaly is identified in real-time. Insome embodiments, the emergency anomaly detection is identified in nearreal-time. In some embodiments, the emergency anomaly detectionalgorithm uses a cluster detection model to identify the emergencyanomaly. In some embodiments, the emergency anomaly detection algorithmexecutes upon each incoming new emergency communication. In someembodiments, the emergency anomaly detection algorithm executesperiodically on a discrete time interval. In some embodiments, theemergency anomaly detection algorithm executes upon receivinginstruction from a user, administrator, an emergency prediction system,an emergency management system, or an emergency dispatch center. In someembodiments, the cluster comprises emergency communications thatcorrespond to a defined geographic area and a defined time period. Infurther embodiments, the defined time period comprises a time block. Inyet further embodiments, the time block is at least about 5 minutes. Inyet further embodiments, the time block is at least about 10 minutes. Inyet further embodiments, the time block is at least about 30 minutes. Inyet further embodiments, the defined time period is determined based onpopulation density and call volume. In further embodiments, the definedgeographic area comprises an area block. In yet further embodiments, thearea block is an area of about 100 square meters. In yet furtherembodiments, the area block is an area of about 1000 square meters. Inyet further embodiments, the area block is an area of about 5 squarekilometers. In yet further embodiments, the area block is an area ofabout 10 square kilometers. In yet further embodiments, the definedgeographic area is determined based on population density and callvolume. In yet further embodiments, the defined geographic areacorresponds to a government defined area. In yet further embodiments,the defined geographic area corresponds to a government defined area. Inyet further embodiments, the defined geographic area is a PSAPjurisdiction, a zip code, a census tract, a city, a county, or anycombination thereof. In yet further embodiments, the emergency datafurther comprises emergency type. In still yet further embodiments, thecluster comprises emergency communications that correspond to a definedgeographic area, a defined time period, and a defined emergency type. Insome embodiments, the emergency data is augmented with environment data.In some embodiments, the emergency data is augmented with event data. Insome embodiments, the emergency anomaly detection algorithm identifiesthe emergency anomaly as being associated with a natural disaster or aman-made disaster. In some embodiments, the emergency anomaly detectionalgorithm identifies the emergency anomaly as being associated with anearthquake, landslide, tsunami, volcanic activity, wildfire, large-scalefire, cyclone, tornado, hurricane, epidemic, extreme temperature,industrial accident, chemical spill, nuclear accident, terrorist attack,or large-scale transport accident. In some embodiments, the emergencyprediction system provides the emergency anomaly to an emergencydispatch center. In further embodiments, the emergency anomaly isprovided as the cluster of emergency communications. In yet furtherembodiments, the emergency prediction system further providesinformation about the cluster comprising a center, a radius, a starttime, an end time, p-value, number of calls, expected number of calls,or any combination thereof. In further embodiments, providing theemergency anomaly comprises displaying the cluster of emergencycommunications on a digital map. In yet further embodiments, theemergency prediction system provides the emergency anomaly in responseto a request from the emergency dispatch center. In yet furtherembodiments, the emergency prediction system provides the emergencyanomaly autonomously. In some embodiments, the emergency predictionsystem locates at least one subject located within the emergency anomalybased on subject mobility data. In further embodiments, the emergencyprediction system sends a notification of the emergency anomaly to theat least one subject located within the emergency anomaly. In someembodiments, the emergency prediction system locates at least onesubject located within the emergency anomaly based on a definedgeographic area and a defined time period of the emergency anomaly. Infurther embodiments, the emergency prediction system sends anotification of the emergency anomaly to the at least one subjectlocated within the emergency anomaly. In some embodiments, emergencyanomaly detection is carried out for a location of a first member devicebelonging to a group of devices authorized to share data. In furtherembodiments, a notification of a detected emergency anomaly is sent to asecond member device in the group of devices. In some embodiments,emergency anomaly detection is carried out for a first member devicebelonging to a group of devices based on information provided by a groupof devices. In further embodiments, a proxy call is initiated on behalfof the first member device when an emergency anomaly is detected for themember device. In yet further embodiments, the proxy call is anemergency call to at least one of an emergency management system and anemergency dispatch center. In further embodiments, a proxy call isinitiated on behalf of the first member device when an emergency anomalyis detected at a location of the first member device. In furtherembodiments, a proxy call is initiated on behalf of the first memberdevice by a second member device in the group of devices when anemergency anomaly is detected for the first member device. In yetfurther embodiments, a location of the first member device is providedto a recipient of the proxy call. In yet further embodiments, theemergency data is provided to a recipient of the proxy call. In someembodiments, the emergency data is obtained from a group of devicescomprising member devices authorized to share data. In some embodiments,the emergency prediction system executes the emergency anomaly detectionalgorithm in response to receiving a request to detect an emergencyanomaly from a communications device. In some embodiments, the emergencyprediction system executes the emergency anomaly detection algorithm inresponse to receiving a request to detect an emergency anomaly from amember device in a group of devices authorized to share data.

In another aspect, disclosed herein are non-transitory computer-readablestorage media encoded with a computer program including instructionsexecutable by at least one processor to create an emergency anomalyprediction application comprising: a) a software module obtainingemergency data for current or ongoing emergency communications, saidemergency data comprising emergency time and emergency location; b) asoftware module obtaining an emergency anomaly detection algorithm formonitoring the emergency communications to identify the emergencyanomaly; and c) a software module executing the emergency anomalydetection algorithm to identify the emergency anomaly based on theemergency data, said emergency anomaly comprising a cluster of emergencycommunications. In some embodiments, the emergency prediction systemuses a multi-class classifier to predict at least one of emergency typeand emergency priority for the cluster of emergency communications. Insome embodiments, the emergency data comprises labeled call data. Insome embodiments, the emergency data comprises unlabeled call data. Insome embodiments, the emergency data comprises augmented call data. Insome embodiments, the emergency anomaly is identified in real-time. Insome embodiments, the emergency anomaly detection is identified in nearreal-time. In some embodiments, the emergency anomaly detectionalgorithm uses a cluster detection model to identify the emergencyanomaly. In some embodiments, the emergency anomaly detection algorithmexecutes upon each incoming new emergency communication. In someembodiments, the emergency anomaly detection algorithm executesperiodically on a discrete time interval. In some embodiments, theemergency anomaly detection algorithm executes upon receivinginstruction from a user, administrator, an emergency prediction system,an emergency management system, or an emergency dispatch center. In someembodiments, the cluster comprises emergency communications thatcorrespond to a defined geographic area and a defined time period. Infurther embodiments, the defined time period comprises a time block. Inyet further embodiments, the time block is at least about 5 minutes. Inyet further embodiments, the time block is at least about 10 minutes. Inyet further embodiments, the time block is at least about 30 minutes. Inyet further embodiments, the defined time period is determined based onpopulation density and call volume. In further embodiments, the definedgeographic area comprises an area block. In yet further embodiments, thearea block is an area of about 100 square meters. In yet furtherembodiments, the area block is an area of about 1000 square meters. Inyet further embodiments, the area block is an area of about 5 squarekilometers. In yet further embodiments, the area block is an area ofabout 10 square kilometers. In yet further embodiments, the definedgeographic area is determined based on population density and callvolume. In yet further embodiments, the defined geographic areacorresponds to a government defined area. In yet further embodiments,the defined geographic area corresponds to a government defined area. Inyet further embodiments, the defined geographic area is a PSAPjurisdiction, a zip code, a census tract, a city, a county, or anycombination thereof. In yet further embodiments, the emergency datafurther comprises emergency type. In still yet further embodiments, thecluster comprises emergency communications that correspond to a definedgeographic area, a defined time period, and a defined emergency type. Insome embodiments, the emergency data is augmented with environment data.In some embodiments, the emergency data is augmented with event data. Insome embodiments, the emergency anomaly detection algorithm identifiesthe emergency anomaly as being associated with a natural disaster or aman-made disaster. In some embodiments, the emergency anomaly detectionalgorithm identifies the emergency anomaly as being associated with anearthquake, landslide, tsunami, volcanic activity, wildfire, large-scalefire, cyclone, tornado, hurricane, epidemic, extreme temperature,industrial accident, chemical spill, nuclear accident, terrorist attack,or large-scale transport accident. In some embodiments, the emergencyprediction system provides the emergency anomaly to an emergencydispatch center. In further embodiments, the emergency anomaly isprovided as the cluster of emergency communications. In yet furtherembodiments, the emergency prediction system further providesinformation about the cluster comprising a center, a radius, a starttime, an end time, p-value, number of calls, expected number of calls,or any combination thereof. In further embodiments, providing theemergency anomaly comprises displaying the cluster of emergencycommunications on a digital map. In yet further embodiments, theemergency prediction system provides the emergency anomaly in responseto a request from the emergency dispatch center. In yet furtherembodiments, the emergency prediction system provides the emergencyanomaly autonomously. In some embodiments, the emergency predictionsystem locates at least one subject located within the emergency anomalybased on subject mobility data. In further embodiments, the emergencyprediction system sends a notification of the emergency anomaly to theat least one subject located within the emergency anomaly. In someembodiments, the emergency prediction system locates at least onesubject located within the emergency anomaly based on a definedgeographic area and a defined time period of the emergency anomaly. Infurther embodiments, the emergency prediction system sends anotification of the emergency anomaly to the at least one subjectlocated within the emergency anomaly. In some embodiments, emergencyanomaly detection is carried out for a location of a first member devicebelonging to a group of devices authorized to share data. In furtherembodiments, a notification of a detected emergency anomaly is sent to asecond member device in the group of devices. In some embodiments,emergency anomaly detection is carried out for a first member devicebelonging to a group of devices based on information provided by a groupof devices. In further embodiments, a proxy call is initiated on behalfof the first member device when an emergency anomaly is detected for themember device. In yet further embodiments, the proxy call is anemergency call to at least one of an emergency management system and anemergency dispatch center. In further embodiments, a proxy call isinitiated on behalf of the first member device when an emergency anomalyis detected at a location of the first member device. In furtherembodiments, a proxy call is initiated on behalf of the first memberdevice by a second member device in the group of devices when anemergency anomaly is detected for the first member device. In yetfurther embodiments, a location of the first member device is providedto a recipient of the proxy call. In yet further embodiments, theemergency data is provided to a recipient of the proxy call. In someembodiments, the emergency data is obtained from a group of devicescomprising member devices authorized to share data. In some embodiments,the emergency prediction system executes the emergency anomaly detectionalgorithm in response to receiving a request to detect an emergencyanomaly from a communications device. In some embodiments, the emergencyprediction system executes the emergency anomaly detection algorithm inresponse to receiving a request to detect an emergency anomaly from amember device in a group of devices authorized to share data.

In another aspect, disclosed herein are emergency resource allocationsystems comprising at least one processor, an operating systemconfigured to perform executable instructions, a memory, and a computerprogram including instructions executable by the at least one processorto create an application comprising: a) a software module obtaining atleast one spatiotemporal emergency prediction; b) a software moduleobtaining at least one estimated response time prediction correspondingto the at least one spatiotemporal emergency prediction; c) a softwaremodule obtaining local emergency resource allocation data; and d) asoftware module using an allocation algorithm to generate arecommendation for optimal allocation of local emergency resources basedon the at least one spatiotemporal emergency prediction, the at leastone estimated response time prediction, and the local emergency resourceallocation data. In some embodiments, the allocation algorithm comprisesa greedy allocation algorithm. In some embodiments, the optimalallocation minimizes a predicted overall emergency response time. Insome embodiments, the optimal allocation minimizes a number of emergencycommunications having an emergency response time exceeding a thresholdtime. In further embodiments, the threshold time is no more than about10 minutes. In further embodiments, the threshold time is no more thanabout 20 minutes. In further embodiments, the local emergency resourcescomprise emergency response vehicle, emergency response personnel,emergency response equipment, emergency response base, or anycombination thereof. In further embodiments, the local emergencyresource allocation data comprises number or amount of local emergencyresources, location of local emergency resources, restraints onallocation of local emergency resources, restraints on dispatch of localemergency resources, or any combination thereof. In further embodiments,the optimal allocation is based on one emergency type. In furtherembodiments, the optimal allocation is based on multiple emergencytypes. In further embodiments, the optimal allocation reduces overallpredicted response time for the spatiotemporal emergency prediction byat least 10%. In further embodiments, the optimal allocation enablesshort-term dynamic reallocation of the local emergency resources. Infurther embodiments, the optimal allocation enables long-term allocationof the local emergency resources. In further embodiments, the optimalallocation is predicted for a defined time period. In yet furtherembodiments, the defined time period comprises a time block. In stillyet further embodiments, the time block is at least about 1 hour. Instill yet further embodiments, the time block is at least about 1 day.In still yet further embodiments, the time block is at least about 1week. In still yet further embodiments, the time block is at least oneday during a week. In still yet further embodiments, the time block isat least one day during a weekend. In still yet further embodiments, thetime block is at least a day in the future. In still yet furtherembodiments, the time block is at least a week in the future. In someembodiments, the optimal allocation is stored on a database. In someembodiments, the emergency resource management system provides theoptimal allocation to an emergency dispatch center. In furtherembodiments, providing the optimal allocation comprises displaying thelocal emergency resources according to the optimal allocation on adigital map. In further embodiments, the emergency resource managementsystem provides the optimal allocation in response to a request from theemergency dispatch center. In further embodiments, the emergencyresource management system provides the optimal allocation to anemergency dispatch center autonomously. In some embodiments, the atleast one prediction model is updated with new emergency data. In someembodiments, the emergency resource management system provides asimulation platform for an administrator to simulate a local emergencyresource allocation. In some embodiments, an estimated response time iscalculated for the local emergency resource allocation provided by thesimulation platform.

In another aspect, provided herein are non-transitory computer-readablestorage media encoded with a computer program including instructionsexecutable by at least one processor to create an emergency resourceallocation application comprising: a) a software module obtaining atleast one spatiotemporal emergency prediction; b) a software moduleobtaining at least one estimated response time prediction correspondingto the at least one spatiotemporal emergency prediction; c) a softwaremodule obtaining local emergency resource allocation data; and d) asoftware module using an allocation algorithm to generate arecommendation for optimal allocation of local emergency resources basedon the at least one spatiotemporal emergency prediction, the at leastone estimated response time prediction, and the local emergency resourceallocation data. In some embodiments, the allocation algorithm comprisesa greedy allocation algorithm. In some embodiments, the optimalallocation minimizes a predicted overall emergency response time. Insome embodiments, the optimal allocation minimizes a number of emergencycommunications having an emergency response time exceeding a thresholdtime. In further embodiments, the threshold time is no more than about10 minutes. In further embodiments, the threshold time is no more thanabout 20 minutes. In further embodiments, the local emergency resourcescomprise emergency response vehicle, emergency response personnel,emergency response equipment, emergency response base, or anycombination thereof. In further embodiments, the local emergencyresource allocation data comprises number or amount of local emergencyresources, location of local emergency resources, restraints onallocation of local emergency resources, restraints on dispatch of localemergency resources, or any combination thereof. In further embodiments,the optimal allocation is based on one emergency type. In furtherembodiments, the optimal allocation is based on multiple emergencytypes. In further embodiments, the optimal allocation reduces overallpredicted response time for the spatiotemporal emergency prediction byat least 10%. In further embodiments, the optimal allocation enablesshort-term dynamic reallocation of the local emergency resources. Infurther embodiments, the optimal allocation enables long-term allocationof the local emergency resources. In further embodiments, the optimalallocation is predicted for a defined time period. In yet furtherembodiments, the defined time period comprises a time block. In stillyet further embodiments, the time block is at least about 1 hour. Instill yet further embodiments, the time block is at least about 1 day.In still yet further embodiments, the time block is at least about 1week. In still yet further embodiments, the time block is at least oneday during a week. In still yet further embodiments, the time block isat least one day during a weekend. In still yet further embodiments, thetime block is at least a day in the future. In still yet furtherembodiments, the time block is at least a week in the future. In someembodiments, the optimal allocation is stored on a database. In someembodiments, the emergency resource management system provides theoptimal allocation to an emergency dispatch center. In furtherembodiments, providing the optimal allocation comprises displaying thelocal emergency resources according to the optimal allocation on adigital map. In further embodiments, the emergency resource managementsystem provides the optimal allocation in response to a request from theemergency dispatch center. In further embodiments, the emergencyresource management system provides the optimal allocation to anemergency dispatch center autonomously. In some embodiments, the atleast one prediction model is updated with new emergency data. In someembodiments, the emergency resource management system provides asimulation platform for an administrator to simulate a local emergencyresource allocation. In some embodiments, an estimated response time iscalculated for the local emergency resource allocation provided by thesimulation platform.

In another aspect, disclosed herein are methods of creating a predictionmodel for generating at least one spatiotemporal emergency prediction,the method comprising: a) obtaining, by an emergency prediction system(EPS), emergency data comprising emergency type, emergency location, andemergency time for a plurality of emergency communications; b)augmenting, by the emergency prediction system, the emergency data withadditional data associated with the plurality of emergencycommunications based on at least one of emergency time, emergencylocation, and calling identity; c) generating, by the emergencyprediction system, at least one prediction model for making at least onespatiotemporal emergency prediction using the emergency data; and d)using, by the emergency prediction system, the at least one predictionmodel to generate at least one spatiotemporal emergency predictioncorresponding to a defined emergency type, a defined geographic area,and a defined time period. Various aspects include one or more of thefollowing elements. In some embodiments, the at least one predictionmodel is generated using a point cloud comprising points sampled from atleast one of current emergency data, proprietary emergency data, and aproprietary data stream. In some embodiments, augmenting the emergencydata with additional data in (b) comprises obtaining additional data,matching at least a subset of the additional data with at least a subsetof the emergency data, and combining information from the at least asubset of the additional data with the at least a subset of theemergency data to form augmented data. In some embodiments, the methodfurther comprises providing, by the emergency prediction system, the atleast one spatiotemporal emergency prediction to an emergency dispatchcenter serving the defined geographic area. In some embodiments,providing the spatiotemporal emergency prediction comprises displaying aset of predicted kernel density estimates on a digital map. In someembodiments, the spatiotemporal emergency prediction is an aggregationof a plurality of spatiotemporal emergency predictions generated forsubsets of the defined geographic area. In some embodiments, the methodfurther comprises using an allocation algorithm to generate arecommendation for optimal allocation of local emergency resources basedon the at least one spatiotemporal emergency prediction. In someembodiments, the at least one prediction model is generated using apoint cloud comprising current emergency data. In further embodiments,the point cloud includes current proprietary emergency data. In someembodiments, the at least one prediction model is generated using apoint cloud comprising a proprietary data stream. In some embodiments,generating the spatiotemporal emergency prediction comprises making andaggregating predictions corresponding to subsets of the defined timeperiod. In some embodiments, generating the spatiotemporal emergencyprediction comprises making and aggregating predictions corresponding tosubsets of the defined geographic area. In some embodiments, thespatiotemporal emergency prediction is used for emergency resourceallocation, anomalous cluster detection, spatiotemporal emergencyprediction visualization, or any combination thereof. In someembodiments, the at least one spatiotemporal emergency predictioncomprises a predicted number of emergency communications. In someembodiments, the at least one spatiotemporal emergency predictioncomprises a predicted emergency communication density. In someembodiments, the at least one spatiotemporal emergency predictioncomprises a set of predicted kernel density estimates. In someembodiments, the at least one spatiotemporal emergency predictioncomprises predicted response time. In some embodiments, the at least onespatiotemporal emergency prediction comprises emergency priority. Insome embodiments, the method further comprises providing, by theemergency prediction system, the spatiotemporal emergency prediction toan emergency dispatch center serving the defined geographic area. Infurther embodiments, providing the spatiotemporal emergency predictioncomprises displaying a set of predicted kernel density estimates on adigital map. In yet further embodiments, the digital map shows at leasta portion of the defined geographic area. In further embodiments, theemergency prediction system provides the spatiotemporal emergencyprediction in response to a request from the emergency dispatch center,an operations center, mapping software, or a connected device. Infurther embodiments, the emergency prediction system provides thespatiotemporal emergency prediction to an emergency dispatch centerautonomously. In some embodiments, the at least one prediction model isupdated with new emergency data. In some embodiments, the at least oneprediction model is trained using labeled call data, unlabeled calldata, augmented call data, or any combination thereof. In someembodiments, the at least one prediction model is assessed forprediction accuracy. In further embodiments, prediction accuracy isdetermined by comparing at least one historical spatiotemporal emergencyprediction to an actual number of emergency communications. In someembodiments, the at least one prediction model is re-created orre-trained. In some embodiments, the at least one prediction model isre-trained at least once a week. In some embodiments, the emergency datacomprises labeled call data. In some embodiments, the emergency datacomprises unlabeled call data. In some embodiments, the emergency datacomprises augmented call data. In further embodiments, the augmentedcall data is obtained by matching unlabeled call data with labeled calldata. In yet further embodiments, matching is based on call identity,emergency time, emergency location, call duration, or any combinationthereof. In yet further embodiments, the unlabeled call data and labeledcall data are merged to form augmented call data. In some embodiments,the emergency data comprises historical emergency data. In someembodiments, the emergency data comprises current emergency data. Insome embodiments, the emergency data comprises public safety answeringpoint (PSAP) call data. In some embodiments, the emergency datacomprises EPS call data. In some embodiments, the emergency data isaugmented with environment data associated with the plurality ofemergency communications based on emergency time and emergency location.In further embodiments, the environment data comprises weatherinformation, traffic information, road condition information, or anycombination thereof. In some embodiments, the emergency data comprisesevent data. In further embodiments, the event data comprises informationon a concert, sporting event, political demonstration, festival,performance, riot, protest, parade, convention, political campaignevent, or any combination thereof. In some embodiments, emergency typeis selected from fire emergency, medical emergency, car accident, policeemergency, and natural disaster. In some embodiments, the emergency timeis a time when an emergency communication is initiated or received or aduration of the emergency communication. In some embodiments, thedefined time period comprises a time block. In further embodiments, thetime block is at least about 1 hour. In further embodiments, the timeblock is at least about 1 day. In further embodiments, the time block isat least about 1 week. In further embodiments, the time block is atleast one day during a week. In further embodiments, the time block isat least one day during a weekend. In some embodiments, the definedgeographic area comprises an area block. In further embodiments, thearea block is an area of about 100 square meters. In furtherembodiments, the area block is an area of about 1000 square meters. Infurther embodiments, the area block is an area of about 5 squarekilometers. In further embodiments, the area block is an area of about10 square kilometers. In some embodiments, the defined geographic areacorresponds to a government defined area. In some embodiments, thedefined geographic area corresponds to a government defined area. Insome embodiments, the defined geographic area is a PSAP jurisdiction, azip code, a census tract, a city, a county, or any combination thereof.In some embodiments, the at least one prediction model comprises aplurality of prediction models corresponding to a plurality of definedgeographic areas, wherein each of the plurality of prediction modelscorresponds to a defined geographic area. In some embodiments, the atleast one prediction model comprises a plurality of prediction modelscorresponding to a plurality of defined emergency types, wherein each ofthe plurality of prediction models corresponds to a defined emergencytype. In some embodiments, a new spatiotemporal emergency prediction isgenerated periodically on a sliding window over time.

In another aspect, disclosed herein are emergency prediction systems(EPS) comprising at least one processor, an operating system configuredto perform executable instructions, a memory, and a computer programincluding instructions executable by the at least one processor tocreate an application comprising: a) a software module obtainingemergency data comprising emergency type, emergency location, andemergency time for a plurality of emergency communications; b) asoftware module augmenting the emergency data with additional dataassociated with the plurality of emergency communications based on atleast one of emergency time, emergency location, and calling identity;c) a software module generating at least one prediction model for makingat least one spatiotemporal emergency prediction using the emergencydata; and d) a software module using the at least one prediction modelto generate at least one spatiotemporal emergency predictioncorresponding to a defined emergency type, a defined geographic area,and a defined time period. Various aspects include one or more of thefollowing elements. In some embodiments, the at least one predictionmodel is generated using a point cloud comprising points sampled from atleast one of current emergency data, proprietary emergency data, and aproprietary data stream. In some embodiments, augmenting the emergencydata with additional data in (b) comprises obtaining additional data,matching at least a subset of the additional data with at least a subsetof the emergency data, and combining information from the at least asubset of the additional data with the at least a subset of theemergency data to form augmented data. In some embodiments, theemergency prediction system provides the at least one spatiotemporalemergency prediction to an emergency dispatch center serving the definedgeographic area. In some embodiments, the emergency prediction systemfurther comprises a software module using an allocation algorithm togenerate a recommendation for optimal allocation of local emergencyresources based on the at least one spatiotemporal emergency prediction.In some embodiments, the at least one prediction model is generatedusing a point cloud comprising current emergency data. In furtherembodiments, the point cloud includes current proprietary emergencydata. In some embodiments, the at least one prediction model isgenerated using a point cloud comprising a proprietary data stream. Insome embodiments, generating the spatiotemporal emergency predictioncomprises making and aggregating predictions corresponding to subsets ofthe defined time period. In some embodiments, generating thespatiotemporal emergency prediction comprises making and aggregatingpredictions corresponding to subsets of the defined geographic area. Insome embodiments, the spatiotemporal emergency prediction is used foremergency resource allocation, anomalous cluster detection,spatiotemporal emergency prediction visualization, or any combinationthereof. In some embodiments, the at least one spatiotemporal emergencyprediction comprises a predicted number of emergency communications. Insome embodiments, the at least one spatiotemporal emergency predictioncomprises a predicted emergency communication density. In someembodiments, the at least one spatiotemporal emergency predictioncomprises a set of predicted kernel density estimates. In someembodiments, the at least one spatiotemporal emergency predictioncomprises predicted response time. In some embodiments, the at least onespatiotemporal emergency prediction comprises emergency priority. Insome embodiments, the emergency prediction system provides thespatiotemporal emergency prediction to an emergency dispatch centerserving the defined geographic area. In further embodiments, providingthe spatiotemporal emergency prediction comprises displaying a set ofpredicted kernel density estimates on a digital map. In yet furtherembodiments, the digital map shows at least a portion of the definedgeographic area. In further embodiments, the emergency prediction systemprovides the spatiotemporal emergency prediction in response to arequest from the emergency dispatch center, an operations center,mapping software, or a connected device. In further embodiments, theemergency prediction system provides the spatiotemporal emergencyprediction to an emergency dispatch center autonomously. In someembodiments, the at least one prediction model is updated with newemergency data. In some embodiments, the at least one prediction modelis trained using labeled call data, unlabeled call data, augmented calldata, or any combination thereof. In some embodiments, the at least oneprediction model is assessed for prediction accuracy. In furtherembodiments, prediction accuracy is determined by comparing at least onehistorical spatiotemporal emergency prediction to an actual number ofemergency communications. In some embodiments, the at least oneprediction model is re-created or re-trained. In some embodiments, theat least one prediction model is re-trained at least once a week. Insome embodiments, the emergency data comprises labeled call data. Insome embodiments, the emergency data comprises unlabeled call data. Insome embodiments, the emergency data comprises augmented call data. Infurther embodiments, the augmented call data is obtained by matchingunlabeled call data with labeled call data. In yet further embodiments,matching is based on call identity, emergency time, emergency location,call duration, or any combination thereof. In yet further embodiments,the unlabeled call data and labeled call data are merged to formaugmented call data. In some embodiments, the emergency data compriseshistorical emergency data. In some embodiments, the emergency datacomprises current emergency data. In some embodiments, the emergencydata comprises public safety answering point (PSAP) call data. In someembodiments, the emergency data comprises EPS call data. In someembodiments, the emergency data is augmented with environment dataassociated with the plurality of emergency communications based onemergency time and emergency location. In further embodiments, theenvironment data comprises weather information, traffic information,road condition information, or any combination thereof. In someembodiments, the emergency data comprises event data. In furtherembodiments, the event data comprises information on a concert, sportingevent, political demonstration, festival, performance, riot, protest,parade, convention, political campaign event, or any combinationthereof. In some embodiments, emergency type is selected from fireemergency, medical emergency, car accident, police emergency, andnatural disaster. In some embodiments, the emergency time is a time whenan emergency communication is initiated or received or a duration of theemergency communication. In some embodiments, the defined time periodcomprises a time block. In further embodiments, the time block is atleast about 1 hour. In further embodiments, the time block is at leastabout 1 day. In further embodiments, the time block is at least about 1week. In further embodiments, the time block is at least one day duringa week. In further embodiments, the time block is at least one dayduring a weekend. In some embodiments, the defined geographic areacomprises an area block. In further embodiments, the area block is anarea of about 100 square meters. In further embodiments, the area blockis an area of about 1000 square meters. In further embodiments, the areablock is an area of about 5 square kilometers. In further embodiments,the area block is an area of about 10 square kilometers. In someembodiments, the defined geographic area corresponds to a governmentdefined area. In some embodiments, the defined geographic areacorresponds to a government defined area. In some embodiments, thedefined geographic area is a PSAP jurisdiction, a zip code, a censustract, a city, a county, or any combination thereof. In someembodiments, the at least one prediction model comprises a plurality ofprediction models corresponding to a plurality of defined geographicareas, wherein each of the plurality of prediction models corresponds toa defined geographic area. In some embodiments, the at least oneprediction model comprises a plurality of prediction modelscorresponding to a plurality of defined emergency types, wherein each ofthe plurality of prediction models corresponds to a defined emergencytype. In some embodiments, a new spatiotemporal emergency prediction isgenerated periodically on a sliding window over time.

In another aspect, disclosed herein are non-transitory computer-readablestorage media encoded with a computer program including instructionsexecutable by at least one processor to create an emergency predictionapplication comprising: a) a software module obtaining emergency datacomprising emergency type, emergency location, and emergency time for aplurality of emergency communications; b) a software module augmentingthe emergency data with additional data associated with the plurality ofemergency communications based on at least one of emergency time,emergency location, and calling identity; c) a software modulegenerating at least one prediction model for making at least onespatiotemporal emergency prediction using the emergency data; and d) asoftware module using the at least one prediction model to generate atleast one spatiotemporal emergency prediction corresponding to a definedemergency type, a defined geographic area, and a defined time period.Various aspects include one or more of the following elements. In someembodiments, the at least one prediction model is generated using apoint cloud comprising points sampled from at least one of currentemergency data, proprietary emergency data, and a proprietary datastream. In some embodiments, augmenting the emergency data withadditional data in (b) comprises obtaining additional data, matching atleast a subset of the additional data with at least a subset of theemergency data, and combining information from the at least a subset ofthe additional data with the at least a subset of the emergency data toform augmented data. In some embodiments, the emergency predictionapplication provides the at least one spatiotemporal emergencyprediction to an emergency dispatch center serving the definedgeographic area. In some embodiments, the emergency predictionapplication further comprises a software module using an allocationalgorithm to generate a recommendation for optimal allocation of localemergency resources based on the at least one spatiotemporal emergencyprediction. In some embodiments, the at least one prediction model isgenerated using a point cloud comprising current emergency data. Infurther embodiments, the point cloud includes current proprietaryemergency data. In some embodiments, the at least one prediction modelis generated using a point cloud comprising a proprietary data stream.In some embodiments, generating the spatiotemporal emergency predictioncomprises making and aggregating predictions corresponding to subsets ofthe defined time period. In some embodiments, generating thespatiotemporal emergency prediction comprises making and aggregatingpredictions corresponding to subsets of the defined geographic area. Insome embodiments, the spatiotemporal emergency prediction is used foremergency resource allocation, anomalous cluster detection,spatiotemporal emergency prediction visualization, or any combinationthereof. In some embodiments, the at least one spatiotemporal emergencyprediction comprises a predicted number of emergency communications. Insome embodiments, the at least one spatiotemporal emergency predictioncomprises a predicted emergency communication density. In someembodiments, the at least one spatiotemporal emergency predictioncomprises a set of predicted kernel density estimates. In someembodiments, the at least one spatiotemporal emergency predictioncomprises predicted response time. In some embodiments, the at least onespatiotemporal emergency prediction comprises emergency priority. Insome embodiments, the emergency prediction application provides thespatiotemporal emergency prediction to an emergency dispatch centerserving the defined geographic area. In further embodiments, providingthe spatiotemporal emergency prediction comprises displaying a set ofpredicted kernel density estimates on a digital map. In yet furtherembodiments, the digital map shows at least a portion of the definedgeographic area. In further embodiments, the emergency prediction systemprovides the spatiotemporal emergency prediction in response to arequest from the emergency dispatch center, an operations center,mapping software, or a connected device. In further embodiments, theemergency prediction system provides the spatiotemporal emergencyprediction to an emergency dispatch center autonomously. In someembodiments, the at least one prediction model is updated with newemergency data. In some embodiments, the at least one prediction modelis trained using labeled call data, unlabeled call data, augmented calldata, or any combination thereof. In some embodiments, the at least oneprediction model is assessed for prediction accuracy. In furtherembodiments, prediction accuracy is determined by comparing at least onehistorical spatiotemporal emergency prediction to an actual number ofemergency communications. In some embodiments, the at least oneprediction model is re-created or re-trained. In some embodiments, theat least one prediction model is re-trained at least once a week. Insome embodiments, the emergency data comprises labeled call data. Insome embodiments, the emergency data comprises unlabeled call data. Insome embodiments, the emergency data comprises augmented call data. Infurther embodiments, the augmented call data is obtained by matchingunlabeled call data with labeled call data. In yet further embodiments,matching is based on call identity, emergency time, emergency location,call duration, or any combination thereof. In yet further embodiments,the unlabeled call data and labeled call data are merged to formaugmented call data. In some embodiments, the emergency data compriseshistorical emergency data. In some embodiments, the emergency datacomprises current emergency data. In some embodiments, the emergencydata comprises public safety answering point (PSAP) call data. In someembodiments, the emergency data comprises EPS call data. In someembodiments, the emergency data is augmented with environment dataassociated with the plurality of emergency communications based onemergency time and emergency location. In further embodiments, theenvironment data comprises weather information, traffic information,road condition information, or any combination thereof. In someembodiments, the emergency data comprises event data. In furtherembodiments, the event data comprises information on a concert, sportingevent, political demonstration, festival, performance, riot, protest,parade, convention, political campaign event, or any combinationthereof. In some embodiments, emergency type is selected from fireemergency, medical emergency, car accident, police emergency, andnatural disaster. In some embodiments, the emergency time is a time whenan emergency communication is initiated or received or a duration of theemergency communication. In some embodiments, the defined time periodcomprises a time block. In further embodiments, the time block is atleast about 1 hour. In further embodiments, the time block is at leastabout 1 day. In further embodiments, the time block is at least about 1week. In further embodiments, the time block is at least one day duringa week. In further embodiments, the time block is at least one dayduring a weekend. In some embodiments, the defined geographic areacomprises an area block. In further embodiments, the area block is anarea of about 100 square meters. In further embodiments, the area blockis an area of about 1000 square meters. In further embodiments, the areablock is an area of about 5 square kilometers. In further embodiments,the area block is an area of about 10 square kilometers. In someembodiments, the defined geographic area corresponds to a governmentdefined area. In some embodiments, the defined geographic areacorresponds to a government defined area. In some embodiments, thedefined geographic area is a PSAP jurisdiction, a zip code, a censustract, a city, a county, or any combination thereof. In someembodiments, the at least one prediction model comprises a plurality ofprediction models corresponding to a plurality of defined geographicareas, wherein each of the plurality of prediction models corresponds toa defined geographic area. In some embodiments, the at least oneprediction model comprises a plurality of prediction modelscorresponding to a plurality of defined emergency types, wherein each ofthe plurality of prediction models corresponds to a defined emergencytype. In some embodiments, a new spatiotemporal emergency prediction isgenerated periodically on a sliding window over time.

In another aspect, disclosed herein are methods for detecting anemergency anomaly, comprising: a) obtaining, by an emergency predictionsystem, emergency data for current emergency communications, saidemergency data comprising emergency time and emergency location; b)providing, by the emergency prediction system, an emergency anomalydetection algorithm for monitoring the emergency communications toidentify the emergency anomaly; and c) executing, by the emergencyprediction system, the emergency anomaly detection algorithm to identifythe emergency anomaly based on the emergency data, said emergencyanomaly comprising a cluster of emergency communications. Variousaspects incorporate one or more of the following. In some embodiments,the method further comprises using a multi-class classifier to predictat least one of emergency type and emergency priority for the cluster ofemergency communications. In some embodiments, the emergency datacomprises labeled call data, unlabeled call data, augmented call data,or any combination thereof. In some embodiments, the emergency anomalyis identified in real-time or near real-time. In some embodiments, theemergency anomaly detection algorithm uses a cluster detection model toidentify the emergency anomaly. In some embodiments, the clustercomprises emergency communications that correspond to a definedgeographic area, a defined time period, and a defined emergency type. Insome embodiments, the method further comprises providing the emergencyanomaly to an emergency dispatch center. In some embodiments, providingthe emergency anomaly comprises displaying the cluster of emergencycommunications on a digital map. In some embodiments, the emergency datacomprises labeled call data. In some embodiments, the emergency datacomprises unlabeled call data. In some embodiments, the emergency datacomprises augmented call data. In some embodiments, the emergencyanomaly is identified in real-time. In some embodiments, the emergencyanomaly detection is identified in near real-time. In some embodiments,the emergency anomaly detection algorithm uses a cluster detection modelto identify the emergency anomaly. In some embodiments, the emergencyanomaly detection algorithm executes upon each incoming new emergencycommunication. In some embodiments, the emergency anomaly detectionalgorithm executes periodically on a discrete time interval. In someembodiments, the emergency anomaly detection algorithm executes uponreceiving instruction from a user, administrator, an emergencyprediction system, an emergency management system, or an emergencydispatch center. In some embodiments, the cluster comprises emergencycommunications that correspond to a defined geographic area and adefined time period. In further embodiments, the defined time periodcomprises a time block. In yet further embodiments, the time block is atleast about 5 minutes. In yet further embodiments, the time block is atleast about 10 minutes. In yet further embodiments, the time block is atleast about 30 minutes. In yet further embodiments, the defined timeperiod is determined based on population density and call volume. Infurther embodiments, the defined geographic area comprises an areablock. In yet further embodiments, the area block is an area of about100 square meters. In yet further embodiments, the area block is an areaof about 1000 square meters. In yet further embodiments, the area blockis an area of about 5 square kilometers. In yet further embodiments, thearea block is an area of about 10 square kilometers. In yet furtherembodiments, the defined geographic area is determined based onpopulation density and call volume. In yet further embodiments, thedefined geographic area corresponds to a government defined area. In yetfurther embodiments, the defined geographic area corresponds to agovernment defined area. In yet further embodiments, the definedgeographic area is a PSAP jurisdiction, a zip code, a census tract, acity, a county, or any combination thereof. In yet further embodiments,the emergency data further comprises emergency type. In still yetfurther embodiments, the cluster comprises emergency communications thatcorrespond to a defined geographic area, a defined time period, and adefined emergency type. In some embodiments, the emergency data isaugmented with environment data. In some embodiments, the emergency datais augmented with event data. In some embodiments, the emergency anomalydetection algorithm identifies the emergency anomaly as being associatedwith a natural disaster or a man-made disaster. In some embodiments, theemergency anomaly detection algorithm identifies the emergency anomalyas being associated with an earthquake, landslide, tsunami, volcanicactivity, wildfire, large-scale fire, cyclone, tornado, hurricane,epidemic, extreme temperature, industrial accident, chemical spill,nuclear accident, terrorist attack, or large-scale transport accident.In some embodiments, the method comprises providing, by the emergencyprediction system, the emergency anomaly to an emergency dispatchcenter. In further embodiments, the emergency anomaly is provided as thecluster of emergency communications. In yet further embodiments, theemergency prediction system further provides information about thecluster comprising a center, a radius, a start time, an end time,p-value, number of calls, expected number of calls, or any combinationthereof. In further embodiments, providing the emergency anomalycomprises displaying the cluster of emergency communications on adigital map. In yet further embodiments, the emergency prediction systemprovides the emergency anomaly in response to a request from theemergency dispatch center. In yet further embodiments, the emergencyprediction system provides the emergency anomaly autonomously. In someembodiments, the method comprises locating, by the emergency predictionsystem, at least one subject located within the emergency anomaly basedon subject mobility data. In further embodiments, the method comprisessending, by the emergency prediction system, a notification of theemergency anomaly to the at least one subject located within theemergency anomaly. In some embodiments, the method comprises locating,by the emergency prediction system, at least one subject located withinthe emergency anomaly based on a defined geographic area and a definedtime period of the emergency anomaly. In further embodiments, the methodcomprises sending, by the emergency prediction system, a notification ofthe emergency anomaly to the at least one subject located within theemergency anomaly. In some embodiments, emergency anomaly detection iscarried out for a location of a first member device belonging to a groupof devices authorized to share data. In further embodiments, anotification of a detected emergency anomaly is sent to a second memberdevice in the group of devices. In some embodiments, emergency anomalydetection is carried out for a first member device belonging to a groupof devices based on information provided by a group of devices. Infurther embodiments, a proxy call is initiated on behalf of the firstmember device when an emergency anomaly is detected for the memberdevice. In yet further embodiments, the proxy call is an emergency callto at least one of an emergency management system and an emergencydispatch center. In further embodiments, a proxy call is initiated onbehalf of the first member device when an emergency anomaly is detectedat a location of the first member device. In further embodiments, aproxy call is initiated on behalf of the first member device by a secondmember device in the group of devices when an emergency anomaly isdetected for the first member device. In yet further embodiments, alocation of the first member device is provided to a recipient of theproxy call. In yet further embodiments, the emergency data is providedto a recipient of the proxy call. In some embodiments, the emergencydata is obtained from a group of devices comprising member devicesauthorized to share data. In some embodiments, the emergency predictionsystem executes the emergency anomaly detection algorithm in response toreceiving a request to detect an emergency anomaly from a communicationsdevice. In some embodiments, the emergency prediction system executesthe emergency anomaly detection algorithm in response to receiving arequest to detect an emergency anomaly from a member device in a groupof devices authorized to share data.

In another aspect, disclosed herein are emergency prediction systems(EPS) comprising at least one processor, an operating system configuredto perform executable instructions, a memory, and a computer programincluding instructions executable by the at least one processor tocreate an application comprising: a) obtaining, by an emergencyprediction system, emergency data for current emergency communications,said emergency data comprising emergency time and emergency location; b)providing, by the emergency prediction system, an emergency anomalydetection algorithm for monitoring the emergency communications toidentify the emergency anomaly; and c) executing, by the emergencyprediction system, the emergency anomaly detection algorithm to identifythe emergency anomaly based on the emergency data, said emergencyanomaly comprising a cluster of emergency communications. Variousaspects incorporate one or more of the following. In some embodiments,the application further comprises using a multi-class classifier topredict at least one of emergency type and emergency priority for thecluster of emergency communications. In some embodiments, the emergencydata comprises labeled call data, unlabeled call data, augmented calldata, or any combination thereof. In some embodiments, the emergencyanomaly is identified in real-time or near real-time. In someembodiments, the emergency anomaly detection algorithm uses a clusterdetection model to identify the emergency anomaly. In some embodiments,the cluster comprises emergency communications that correspond to adefined geographic area, a defined time period, and a defined emergencytype. In some embodiments, the application further comprises providingthe emergency anomaly to an emergency dispatch center. In someembodiments, providing the emergency anomaly comprises displaying thecluster of emergency communications on a digital map. In someembodiments, the emergency data comprises labeled call data. In someembodiments, the emergency data comprises unlabeled call data. In someembodiments, the emergency data comprises augmented call data. In someembodiments, the emergency anomaly is identified in real-time. In someembodiments, the emergency anomaly detection is identified in nearreal-time. In some embodiments, the emergency anomaly detectionalgorithm uses a cluster detection model to identify the emergencyanomaly. In some embodiments, the emergency anomaly detection algorithmexecutes upon each incoming new emergency communication. In someembodiments, the emergency anomaly detection algorithm executesperiodically on a discrete time interval. In some embodiments, theemergency anomaly detection algorithm executes upon receivinginstruction from a user, administrator, an emergency prediction system,an emergency management system, or an emergency dispatch center. In someembodiments, the cluster comprises emergency communications thatcorrespond to a defined geographic area and a defined time period. Infurther embodiments, the defined time period comprises a time block. Inyet further embodiments, the time block is at least about 5 minutes. Inyet further embodiments, the time block is at least about 10 minutes. Inyet further embodiments, the time block is at least about 30 minutes. Inyet further embodiments, the defined time period is determined based onpopulation density and call volume. In further embodiments, the definedgeographic area comprises an area block. In yet further embodiments, thearea block is an area of about 100 square meters. In yet furtherembodiments, the area block is an area of about 1000 square meters. Inyet further embodiments, the area block is an area of about 5 squarekilometers. In yet further embodiments, the area block is an area ofabout 10 square kilometers. In yet further embodiments, the definedgeographic area is determined based on population density and callvolume. In yet further embodiments, the defined geographic areacorresponds to a government defined area. In yet further embodiments,the defined geographic area corresponds to a government defined area. Inyet further embodiments, the defined geographic area is a PSAPjurisdiction, a zip code, a census tract, a city, a county, or anycombination thereof. In yet further embodiments, the emergency datafurther comprises emergency type. In still yet further embodiments, thecluster comprises emergency communications that correspond to a definedgeographic area, a defined time period, and a defined emergency type. Insome embodiments, the emergency data is augmented with environment data.In some embodiments, the emergency data is augmented with event data. Insome embodiments, the emergency anomaly detection algorithm identifiesthe emergency anomaly as being associated with a natural disaster or aman-made disaster. In some embodiments, the emergency anomaly detectionalgorithm identifies the emergency anomaly as being associated with anearthquake, landslide, tsunami, volcanic activity, wildfire, large-scalefire, cyclone, tornado, hurricane, epidemic, extreme temperature,industrial accident, chemical spill, nuclear accident, terrorist attack,or large-scale transport accident. In some embodiments, the emergencyprediction system provides the emergency anomaly to an emergencydispatch center. In further embodiments, the emergency anomaly isprovided as the cluster of emergency communications. In yet furtherembodiments, the emergency prediction system further providesinformation about the cluster comprising a center, a radius, a starttime, an end time, p-value, number of calls, expected number of calls,or any combination thereof. In further embodiments, providing theemergency anomaly comprises displaying the cluster of emergencycommunications on a digital map. In yet further embodiments, theemergency prediction system provides the emergency anomaly in responseto a request from the emergency dispatch center. In yet furtherembodiments, the emergency prediction system provides the emergencyanomaly autonomously. In some embodiments, the emergency predictionsystem locates at least one subject located within the emergency anomalybased on subject mobility data. In further embodiments, the emergencyprediction system sends a notification of the emergency anomaly to theat least one subject located within the emergency anomaly. In someembodiments, the emergency prediction system locates at least onesubject located within the emergency anomaly based on a definedgeographic area and a defined time period of the emergency anomaly. Infurther embodiments, the emergency prediction system sends anotification of the emergency anomaly to the at least one subjectlocated within the emergency anomaly.

In another aspect, disclosed herein are non-transitory computer-readablestorage media encoded with a computer program including instructionsexecutable by at least one processor to create an emergency predictionapplication comprising: a) obtaining, by an emergency prediction system,emergency data for current emergency communications, said emergency datacomprising emergency time and emergency location; b) providing, by theemergency prediction system, an emergency anomaly detection algorithmfor monitoring the emergency communications to identify the emergencyanomaly; and c) executing, by the emergency prediction system, theemergency anomaly detection algorithm to identify the emergency anomalybased on the emergency data, said emergency anomaly comprising a clusterof emergency communications. Various aspects incorporate one or more ofthe following. In some embodiments, the application further comprisesusing a multi-class classifier to predict at least one of emergency typeand emergency priority for the cluster of emergency communications. Insome embodiments, the emergency data comprises labeled call data,unlabeled call data, augmented call data, or any combination thereof. Insome embodiments, the emergency anomaly is identified in real-time ornear real-time. In some embodiments, the emergency anomaly detectionalgorithm uses a cluster detection model to identify the emergencyanomaly. In some embodiments, the cluster comprises emergencycommunications that correspond to a defined geographic area, a definedtime period, and a defined emergency type. In some embodiments, theapplication further comprises providing the emergency anomaly to anemergency dispatch center. In some embodiments, providing the emergencyanomaly comprises displaying the cluster of emergency communications ona digital map. In some embodiments, the emergency data comprises labeledcall data. In some embodiments, the emergency data comprises unlabeledcall data. In some embodiments, the emergency data comprises augmentedcall data. In some embodiments, the emergency anomaly is identified inreal-time. In some embodiments, the emergency anomaly detection isidentified in near real-time. In some embodiments, the emergency anomalydetection algorithm uses a cluster detection model to identify theemergency anomaly. In some embodiments, the emergency anomaly detectionalgorithm executes upon each incoming new emergency communication. Insome embodiments, the emergency anomaly detection algorithm executesperiodically on a discrete time interval. In some embodiments, theemergency anomaly detection algorithm executes upon receivinginstruction from a user, administrator, an emergency prediction system,an emergency management system, or an emergency dispatch center. In someembodiments, the cluster comprises emergency communications thatcorrespond to a defined geographic area and a defined time period. Infurther embodiments, the defined time period comprises a time block. Inyet further embodiments, the time block is at least about 5 minutes. Inyet further embodiments, the time block is at least about 10 minutes. Inyet further embodiments, the time block is at least about 30 minutes. Inyet further embodiments, the defined time period is determined based onpopulation density and call volume. In further embodiments, the definedgeographic area comprises an area block. In yet further embodiments, thearea block is an area of about 100 square meters. In yet furtherembodiments, the area block is an area of about 1000 square meters. Inyet further embodiments, the area block is an area of about 5 squarekilometers. In yet further embodiments, the area block is an area ofabout 10 square kilometers. In yet further embodiments, the definedgeographic area is determined based on population density and callvolume. In yet further embodiments, the defined geographic areacorresponds to a government defined area. In yet further embodiments,the defined geographic area corresponds to a government defined area. Inyet further embodiments, the defined geographic area is a PSAPjurisdiction, a zip code, a census tract, a city, a county, or anycombination thereof. In yet further embodiments, the emergency datafurther comprises emergency type. In still yet further embodiments, thecluster comprises emergency communications that correspond to a definedgeographic area, a defined time period, and a defined emergency type. Insome embodiments, the emergency data is augmented with environment data.In some embodiments, the emergency data is augmented with event data. Insome embodiments, the emergency anomaly detection algorithm identifiesthe emergency anomaly as being associated with a natural disaster or aman-made disaster. In some embodiments, the emergency anomaly detectionalgorithm identifies the emergency anomaly as being associated with anearthquake, landslide, tsunami, volcanic activity, wildfire, large-scalefire, cyclone, tornado, hurricane, epidemic, extreme temperature,industrial accident, chemical spill, nuclear accident, terrorist attack,or large-scale transport accident. In some embodiments, the emergencyprediction system provides the emergency anomaly to an emergencydispatch center. In further embodiments, the emergency anomaly isprovided as the cluster of emergency communications. In yet furtherembodiments, the emergency prediction system further providesinformation about the cluster comprising a center, a radius, a starttime, an end time, p-value, number of calls, expected number of calls,or any combination thereof. In further embodiments, providing theemergency anomaly comprises displaying the cluster of emergencycommunications on a digital map. In yet further embodiments, theemergency prediction system provides the emergency anomaly in responseto a request from the emergency dispatch center. In yet furtherembodiments, the emergency prediction system provides the emergencyanomaly autonomously. In some embodiments, the emergency predictionsystem locates at least one subject located within the emergency anomalybased on subject mobility data. In further embodiments, the emergencyprediction system sends a notification of the emergency anomaly to theat least one subject located within the emergency anomaly. In someembodiments, the emergency prediction system locates at least onesubject located within the emergency anomaly based on a definedgeographic area and a defined time period of the emergency anomaly. Infurther embodiments, the emergency prediction system sends anotification of the emergency anomaly to the at least one subjectlocated within the emergency anomaly.

INCORPORATION BY REFERENCE

All publications, patents, and patent applications mentioned in thisspecification are herein incorporated by reference to the same extent asif each individual publication, patent, or patent application wasspecifically and individually indicated to be incorporated by reference.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features of the invention are set forth with particularity inthe appended claims. A better understanding of the features andadvantages of the present invention will be obtained by reference to thefollowing detailed description that sets forth illustrative embodiments,in which the principles of the invention are utilized, and theaccompanying drawings of which:

FIG. 1 illustrates exemplary system components for one embodiment of anemergency prediction system;

FIG. 2 illustrates an exemplary system flow for one embodiment of anemergency prediction system;

FIGS. 3A and 3B illustrate exemplary method for augmenting emergencycall data and call data stream;

FIGS. 4A and 4B depict schematic diagrams for implementing oneembodiment of a method for generating spatiotemporal emergencyprediction;

FIG. 5A depicts a method for generating spatiotemporal emergencyprediction using kernel warping technique;

FIGS. 5B and 5C depict exemplary input emergency call data and exemplaryoutput kernel densities on a map;

FIGS. 6A and 6B depict schematic diagrams for implementing oneembodiment of a method for detecting an emergency event or anomaly;

FIG. 7 illustrates an exemplary method for emergency resourceallocation;

FIG. 8 shows an exemplary set of validation data;

FIG. 9 illustrates one embodiment of an emergency prediction system forautonomously predicting emergencies involving a user's communicationdevices;

FIG. 10 illustrates one embodiment of an emergency prediction system forautonomously predicting emergencies involving a group of users;

FIG. 11 depicts a method for real-time or near real-time emergencyprediction and notification;

FIG. 12 illustrates one embodiment of an emergency prediction system forautonomously predicting emergencies involving a group of users indifferent PSAP service areas;

FIG. 13 depicts a method for sending a request for assistance based on athreat of an emergency involving a group of users; and

FIGS. 14A-14D depict components of embodiments of the emergencyprediction system.

DETAILED DESCRIPTION

In certain embodiments, disclosed herein are methods of creating aprediction model for generating at least one spatiotemporal emergencyprediction, the methods comprising: a) obtaining, by an emergencyprediction system (EPS), emergency data comprising emergency type,emergency location, and emergency time for a plurality of emergencycommunications; b) generating, by the emergency prediction system, atleast one prediction model for making at least one spatiotemporalemergency prediction using the emergency data; and c) using, by theemergency prediction system, the at least one prediction model togenerate a spatiotemporal emergency prediction corresponding to adefined emergency type, a defined geographic area, and a defined timeperiod.

In another aspect, disclosed herein are methods of creating a predictionmodel for generating at least one spatiotemporal emergency prediction,the methods comprising: a) obtaining, by an emergency prediction system(EPS), emergency data for a plurality of emergencies, the emergency datacomprising emergency location and emergency time; b) generating, by theemergency prediction system, at least one prediction model for making atleast one spatiotemporal emergency prediction using the emergency data;and c) using, by the emergency prediction system, the at least oneprediction model to generate a spatiotemporal emergency predictioncorresponding to a defined geographic area and a defined time period.

In certain embodiments, disclosed herein are computer-implementedmethods for augmenting unlabeled emergency data, comprising: a)obtaining, by an emergency prediction system, unlabeled emergency data;b) obtaining, by the emergency prediction system, historical labeledemergency data originating from at least one emergency dispatch center,said historical labeled emergency data comprising emergency type andemergency priority; c) matching, by the emergency prediction system, atleast a subset of the unlabeled emergency data with at least a subset ofthe historical labeled emergency data; and d) merging, by the emergencyprediction system, the at least a subset of the unlabeled emergency datawith the at least a subset of the historical labeled emergency data togenerate matched emergency data.

In certain embodiments, disclosed herein are computer-implementedmethods for predicting labels for an emergency communication datastream, the methods comprising: a) obtaining, by an emergency predictionsystem, unlabeled emergency data; b) obtaining, by the emergencyprediction system, historical labeled emergency data originating from atleast one emergency dispatch center; c) matching, by the emergencyprediction system, at least a subset of the unlabeled emergency datawith at least a subset of the historical labeled emergency data togenerate matched emergency data; d) training, by the emergencyprediction system, a prediction algorithm using the matched emergencydata; and e) using, by the emergency prediction system, the predictionalgorithm to predict labels for an incoming data stream of unlabeledemergency data.

In certain embodiments, disclosed herein are methods for detecting anemergency anomaly, the methods comprising: a) obtaining, by an emergencyprediction system, emergency data for current or ongoing emergencycommunications, said emergency data comprising emergency time andemergency location; b) providing, by the emergency prediction system, anemergency anomaly detection algorithm for monitoring the emergencycommunications to identify the emergency anomaly; and c) executing, bythe emergency prediction system, the emergency anomaly detectionalgorithm to identify the emergency anomaly based on the emergency data,said emergency anomaly comprising a cluster of emergency communications.

In certain embodiments, disclosed herein are methods for optimizingemergency resource allocation using emergency data, comprising: a)obtaining, by an emergency resource management system, at least onespatiotemporal emergency prediction; b) obtaining, by the emergencyresource management system, at least one estimated response timeprediction corresponding to the at least one spatiotemporal emergencyprediction; c) obtaining, by the emergency resource management system,local emergency resource allocation data; and d) using, by the emergencyresource management system, an allocation algorithm to generate arecommendation for optimal allocation of local emergency resources basedon the at least one spatiotemporal emergency prediction, the at leastone estimated response time prediction, and the local emergency resourceallocation data.

In certain embodiments, disclosed herein are emergency predictionsystems (EPS) comprising at least one processor, an operating systemconfigured to perform executable instructions, a memory, and a computerprogram including instructions executable by the at least one processorto create an application comprising: a) a software module obtainingemergency data comprising emergency type, emergency location, andemergency time for a plurality of emergency communications; b) asoftware module generating at least one prediction model for making atleast one spatiotemporal emergency prediction using the emergency data;and c) a software module using the at least one prediction model togenerate a spatiotemporal emergency prediction corresponding to adefined emergency type, a defined geographic area, and a defined timeperiod.

In certain embodiments, disclosed herein are emergency predictionsystems (EPS) comprising at least one processor, an operating systemconfigured to perform executable instructions, a memory, and a computerprogram including instructions executable by the at least one processorto create an application comprising: a) a software module obtainingemergency data for a plurality of emergencies, the emergency datacomprising emergency location and emergency time; b) a software modulegenerating at least one prediction model for making at least onespatiotemporal emergency prediction using the emergency data; and c) asoftware module using the at least one prediction model to generate aspatiotemporal emergency prediction corresponding to a definedgeographic area and a defined time period.

In certain embodiments, disclosed herein are non-transitorycomputer-readable storage media encoded with a computer programincluding instructions executable by at least one processor to create anemergency prediction application comprising: a) a software moduleobtaining emergency data comprising emergency type, emergency location,and emergency time for a plurality of emergency communications; b) asoftware module generating at least one prediction model for making atleast one spatiotemporal emergency prediction using the emergency data;and c) a software module using the at least one prediction model togenerate a spatiotemporal emergency prediction corresponding to adefined emergency type, a defined geographic area, and a defined timeperiod. In some embodiments, the at least one prediction model isgenerated using a point cloud comprising current emergency data.

In certain embodiments, disclosed herein are non-transitorycomputer-readable storage media encoded with a computer programincluding instructions executable by at least one processor to create anemergency prediction application comprising: a) a software moduleobtaining emergency data for a plurality of emergencies, the emergencydata comprising emergency location and emergency time; b) a softwaremodule generating at least one prediction model for making at least onespatiotemporal emergency prediction using the emergency data; and c) asoftware module using the at least one prediction model to generate aspatiotemporal emergency prediction corresponding to a definedgeographic area and a defined time period.

In certain embodiments, disclosed herein are emergency predictionsystems comprising: at least one processor, an operating systemconfigured to perform executable instructions, a memory, and a computerprogram including instructions executable by the at least one processorto create an application comprising: a) a software module obtainingunlabeled emergency data; b) a software module obtaining historicallabeled emergency data originating from at least one emergency dispatchcenter, said historical labeled emergency data comprising emergency typeand emergency priority; c) a software module matching at least a subsetof the unlabeled emergency data with at least a subset of the historicallabeled emergency data; and d) a software module merging the at least asubset of the unlabeled emergency data with the at least a subset of thehistorical labeled emergency data to generate matched emergency data.

In certain embodiments, disclosed herein are non-transitorycomputer-readable storage media encoded with a computer programincluding instructions executable by at least one processor to create anemergency prediction application comprising: a) a software moduleobtaining unlabeled emergency data; b) a software module obtaininghistorical labeled emergency data originating from at least oneemergency dispatch center, said historical labeled emergency datacomprising emergency type and emergency priority; c) a software modulematching at least a subset of the unlabeled emergency data with at leasta subset of the historical labeled emergency data; and d) a softwaremodule merging the at least a subset of the unlabeled emergency datawith the at least a subset of the historical labeled emergency data togenerate matched emergency data.

In certain embodiments, disclosed herein are emergency anomalyprediction systems comprising at least one processor, an operatingsystem configured to perform executable instructions, a memory, and acomputer program including instructions executable by the at least oneprocessor to create an application comprising: a) a software moduleobtaining emergency data for current or ongoing emergencycommunications, said emergency data comprising emergency time andemergency location; b) a software module obtaining an emergency anomalydetection algorithm for monitoring the emergency communications toidentify the emergency anomaly; and c) a software module executing theemergency anomaly detection algorithm to identify the emergency anomalybased on the emergency data, said emergency anomaly comprising a clusterof emergency communications.

In certain embodiments, disclosed herein are non-transitorycomputer-readable storage media encoded with a computer programincluding instructions executable by at least one processor to create anemergency anomaly prediction application comprising: a) a softwaremodule obtaining emergency data for current or ongoing emergencycommunications, said emergency data comprising emergency time andemergency location; b) a software module obtaining an emergency anomalydetection algorithm for monitoring the emergency communications toidentify the emergency anomaly; and c) a software module executing theemergency anomaly detection algorithm to identify the emergency anomalybased on the emergency data, said emergency anomaly comprising a clusterof emergency communications.

In certain embodiments, disclosed herein are emergency resourceallocation systems comprising at least one processor, an operatingsystem configured to perform executable instructions, a memory, and acomputer program including instructions executable by the at least oneprocessor to create an application comprising: a) a software moduleobtaining at least one spatiotemporal emergency prediction; b) asoftware module obtaining at least one estimated response timeprediction corresponding to the at least one spatiotemporal emergencyprediction; c) a software module obtaining local emergency resourceallocation data; and d) a software module using an allocation algorithmto generate a recommendation for optimal allocation of local emergencyresources based on the at least one spatiotemporal emergency prediction,the at least one estimated response time prediction, and the localemergency resource allocation data.

In certain embodiments, provided herein are non-transitorycomputer-readable storage media encoded with a computer programincluding instructions executable by at least one processor to create anemergency resource allocation application comprising: a) a softwaremodule obtaining at least one spatiotemporal emergency prediction; b) asoftware module obtaining at least one estimated response timeprediction corresponding to the at least one spatiotemporal emergencyprediction; c) a software module obtaining local emergency resourceallocation data; and d) a software module using an allocation algorithmto generate a recommendation for optimal allocation of local emergencyresources based on the at least one spatiotemporal emergency prediction,the at least one estimated response time prediction, and the localemergency resource allocation data.

In certain embodiments, disclosed herein are methods of creating aprediction model for generating at least one spatiotemporal emergencyprediction, the method comprising: a) obtaining, by an emergencyprediction system (EPS), emergency data comprising emergency type,emergency location, and emergency time for a plurality of emergencycommunications; b) augmenting, by the emergency prediction system, theemergency data with additional data associated with the plurality ofemergency communications based on at least one of emergency time,emergency location, and calling identity; c) generating, by theemergency prediction system, at least one prediction model for making atleast one spatiotemporal emergency prediction using the emergency data;and d) using, by the emergency prediction system, the at least oneprediction model to generate at least one spatiotemporal emergencyprediction corresponding to a defined emergency type, a definedgeographic area, and a defined time period.

In certain embodiments, disclosed herein are emergency predictionsystems (EPS) comprising at least one processor, an operating systemconfigured to perform executable instructions, a memory, and a computerprogram including instructions executable by the at least one processorto create an application comprising: a) a software module obtainingemergency data comprising emergency type, emergency location, andemergency time for a plurality of emergency communications; b) asoftware module augmenting the emergency data with additional dataassociated with the plurality of emergency communications based on atleast one of emergency time, emergency location, and calling identity;c) a software module generating at least one prediction model for makingat least one spatiotemporal emergency prediction using the emergencydata; and d) a software module using the at least one prediction modelto generate at least one spatiotemporal emergency predictioncorresponding to a defined emergency type, a defined geographic area,and a defined time period.

In certain embodiments, disclosed herein are non-transitorycomputer-readable storage media encoded with a computer programincluding instructions executable by at least one processor to create anemergency prediction application comprising: a) a software moduleobtaining emergency data comprising emergency type, emergency location,and emergency time for a plurality of emergency communications; b) asoftware module augmenting the emergency data with additional dataassociated with the plurality of emergency communications based on atleast one of emergency time, emergency location, and calling identity;c) a software module generating at least one prediction model for makingat least one spatiotemporal emergency prediction using the emergencydata; and d) a software module using the at least one prediction modelto generate at least one spatiotemporal emergency predictioncorresponding to a defined emergency type, a defined geographic area,and a defined time period.

In certain embodiments, disclosed herein are methods for detecting anemergency anomaly, comprising: a) obtaining, by an emergency predictionsystem, emergency data for current emergency communications, saidemergency data comprising emergency time and emergency location; b)providing, by the emergency prediction system, an emergency anomalydetection algorithm for monitoring the emergency communications toidentify the emergency anomaly; and c) executing, by the emergencyprediction system, the emergency anomaly detection algorithm to identifythe emergency anomaly based on the emergency data, said emergencyanomaly comprising a cluster of emergency communications.

In certain embodiments, disclosed herein are emergency predictionsystems (EPS) comprising at least one processor, an operating systemconfigured to perform executable instructions, a memory, and a computerprogram including instructions executable by the at least one processorto create an application comprising: a) obtaining, by an emergencyprediction system, emergency data for current emergency communications,said emergency data comprising emergency time and emergency location; b)providing, by the emergency prediction system, an emergency anomalydetection algorithm for monitoring the emergency communications toidentify the emergency anomaly; and c) executing, by the emergencyprediction system, the emergency anomaly detection algorithm to identifythe emergency anomaly based on the emergency data, said emergencyanomaly comprising a cluster of emergency communications.

In certain embodiments, disclosed herein are non-transitorycomputer-readable storage media encoded with a computer programincluding instructions executable by at least one processor to create anemergency prediction application comprising: a) obtaining, by anemergency prediction system, emergency data for current emergencycommunications, said emergency data comprising emergency time andemergency location; b) providing, by the emergency prediction system, anemergency anomaly detection algorithm for monitoring the emergencycommunications to identify the emergency anomaly; and c) executing, bythe emergency prediction system, the emergency anomaly detectionalgorithm to identify the emergency anomaly based on the emergency data,said emergency anomaly comprising a cluster of emergency communications.

Aspects and embodiments disclosed herein are not limited to the detailsof construction and the arrangement of components set forth in thefollowing description or illustrated in the drawings. Aspects andembodiments disclosed herein are capable of being practiced or of beingcarried out in various ways. Also, the phraseology and terminology usedherein is for the purpose of description and should not be regarded aslimiting. The use of “including,” “comprising,” “having,” “containing,”“involving,” and variations thereof herein is meant to encompass theitems listed thereafter and equivalents thereof as well as additionalitems.

Existing filings, for example, PCT application No. PCT/US2015/050609,titled METHOD AND SYSTEM FOR EMERGENCY CALL MANAGEMENT, disclosesystems, methods, and media that take advantage of Voice over InternetProtocol (VoIP) technology to make emergency calls to EDCs that includeindications of the exact geographic locations of subject communicationdevices used to place the emergency calls.

Certain Terminologies

As referenced herein, an “emergency prediction system” refers to asystem that applies a prediction algorithm or prediction model to data(e.g., historical emergency, environmental, and event data) in order togenerate predictions.

As referenced herein, an “emergency communication” or “emergency call”refers to a communication between a user and a recipient of thecommunication. In some embodiments, the recipient is an emergencyservice (e.g., EDC, PSAP, emergency response personnel, etc.). In someembodiments, the recipient is a public emergency service or a privateemergency service. In some embodiments, the recipient is a non-emergencyservice. In some embodiments, the recipient is a an emergency operationscenter like a fleet management group, corporate security ops center, orinsurance company management center. In some embodiments, thecommunication is a digital or analog phone call. In some embodiments,the communication is a message such as a text message or SMS. In someembodiments, the communication is a data stream. In some embodiments,the communication is a phone call, a message, a data stream, or anycombination thereof. In some embodiments, the communication comprises aphone call, a message, a data stream, or any combination thereof.

As referenced herein, “call data” refers to information associated witha communication between a user and a recipient. In some embodiments,call data includes labeled call data, unlabeled call data, matched calldata, augmented call data, or other forms of information regarding thecommunication. As used herein, a communication is not limited to voiceor data calls. In some embodiments, the communication comprises analert, a message, a data packet, a data stream, or other forms ofdigital or analog communications. Accordingly, in some embodiments, calldata includes non-call information.

As referenced herein, “municipalities” and “counties” refer to a localgovernment or an administrative division of a state that will beresponsible for providing dispatchers, first responders, or emergencyresponse personnel during emergency situations. A “county” refers to apolitical and administrative division of a state in both urban and ruralareas. In contrast, a “municipality” refers to a town or district thathas local government particularly in population centers includingincorporated cities, towns, villages and other types of municipalities.Depending on the location, emergency response for different types ofemergencies may be provided by either the municipality or the countyadministration.

As referenced herein, “emergency service providers” may includeorganizations and institutions that may provide assistance in anemergency. For example, law enforcement, fire, emergency medicalservices commonly handle many emergency requests. In addition,specialized services may also be included, such as Coast Guard,Emergency management, HAZ-MAT, Emergency roadside assistance, animalcontrol, poison control, social services, etc. Emergency serviceproviders, emergency response personnel, emergency dispatch center, andpublic safety access points may be used to refer to the organizations,systems, and/or personnel that provide emergency response servicesand/or coordination of such services.

As referenced herein, an “Emergency Management System (“EMS”) refers toa system that receives and processes emergency alerts from subjects andforwards them to the EDC. Various embodiments of the EMS are describedin U.S. patent application Ser. No. 14/856,818, and incorporated hereinby reference. The “Emergency Dispatch Center (“EDC”) refers to theentity that receives the emergency alert and coordinates the emergencyassistance. In some embodiments, the EDC is a public organization run bythe municipality, county, or city, or alternatively, is a privateorganization. In some embodiments, emergency assistance (e.g., emergencyresponse personnel and/or resources) is provided is in the form ofmedical, caregiver(s), firefighting, police, military, paramilitary,border patrol, lifeguard, security services, or any combination thereof.In some embodiments, an EDC is a public safety answering point (“PSAP”).Generally, the EDC and EMS are distinct entities. In some embodiments,the EDC comprises an EMS integrated into the EDC.

As referenced herein, “predictive analytics” refers to the use ofstatistical and/or modeling techniques to predict future events based oncurrent and/or historical data. In some embodiments, predictiveanalytics also refers to methods used to mathematically describe events.In some embodiments, predictive analytics also refers to analyticalmethods used to make decisions and optimize processes.

As referenced herein, “geographic area,” “geographic location,” “area,”“location,” all refer to a geographic point or area. In someembodiments, a location is an exact latitudinal and longitudinalcoordinate or an area encompassing, for example, a city block, aneighborhood, a city, a county, a stretch of highway, a park, arecreation area, a sports stadium, a convention center, an area block(e.g., a 1×1 square mile area block), or other area. In someembodiments, a “geographic area” is used in the context of a “definedgeographic area” corresponding to a risk prediction. In someembodiments, a geographic area comprises one or more locations. Forexample, in some embodiments, a defined geographic area that is a countycomprises a plurality of neighborhood locations.

As referenced herein, “data” refers to electronic information. In someembodiments, data is emergency data (e.g., data relating to one or moreemergencies or emergency communications). In some embodiments, data isdivided into current and historical data. In some embodiments, currentdata refers to relatively recent data (e.g., within the past 15minutes), while historical data refers to relatively old data (e.g.,older than 15 minutes). In some embodiments, current data comprisesreal-time and near real-time data.

As referenced herein, “data stream” refers to incoming data being sentand/or received. In some embodiments, a data stream comprises currentemergency data. In some embodiments, a data stream refers to incomingemergency communications/data.

As used herein, “variable” refers to a parameter used within a model.For example, a linear regression model having a formulaY=C₀+C_(1X1)+C_(2X2) has two predictor variables or parameters, x1 andx2, and coefficients for each parameter, C₁ and C₂ respectively. Thepredicted variable in this example is Y. In some embodiments, values areentered for each predictor variable or parameter in a model to generatea result for the dependent or predicted variable (e.g., Y).

As used herein, “average” refers to a statistical measure of a pluralityof values. In some embodiments, an average is mean, median, or mode.

As used herein, “calling identity” refers to information that is used toidentify a caller or calling device. In some embodiments, callingidentity includes user name, phone number, email address, calling deviceidentifier, network identifier, IP address, Electronic Serial Number,Media Access Control (MAC) address, Temporary Mobile Station Identifier(TMSI), IP address, or other identifying information. In someembodiments, calling device is not limited to devices making phone callsand includes devices sending emergency alerts or other emergencycommunications.

As used herein, “risk” refers to the likelihood of occurrence of anemergency, emergency event, or emergency request. A “risk prediction”refers to a predicted likelihood of occurrence of an emergency,emergency event, or emergency request corresponding to at least one of adefined emergency type, a defined geographic area, or a defined timeperiod that is generated by one or more prediction models describedherein.

For example, a risk prediction for traffic accident emergencies (definedemergency) in county A (defined geographic area) during the time periodof 12 PM-9 PM on a non-holiday weekday (defined time period) may beabout 24 emergency requests (risk prediction). A risk prediction iscalculated using a prediction model generated by an algorithm. Thealgorithm may statistical tools/methods to compare historical data foremergencies with historical data on environmental conditions and eventsin order to generate a prediction model that correlates the relationshipbetween environmental conditions and/or events with the number ofemergencies. The algorithm may also comprise machine learning methods ingenerating the prediction model. In some embodiments, a prediction modelis a formula comprising parameters that determine the likelihood of adefined emergency. For example, in some embodiments, a prediction modelis a multiple linear regression model or formula that generates a riskprediction for the total number of all emergency calls (includingemergency incidents) within the city limits of city B for next Fridaywhen data corresponding to environmental condition(s) (e.g., expectedrainfall) and/or event(s) (e.g., grand opening of a museum downtown)inside city B next Friday is entered into the model. In someembodiments, a prediction model is a classifier or trained algorithmgenerated by the application of a machine learning algorithm to a dataset comprising emergency, environmental, and event data.

As used herein, “notification,” “warning,” or “warning signal” refers toa message containing information of predictions. In some embodiments, anotification or warning comprises additional information, such as, forexample, advice for escaping, resolving, mitigating, or reducing thelikelihood of occurrence of the risk or emergency situation.

Predictive Analytics Overview

The systems, methods, and media provided in the present disclosure asdescribed herein allow for the application of an algorithm towardsemergency data to generate one or more prediction models for makingpredictions. In some embodiments, a prediction comprises a predictednumber of emergencies or emergency communications. In some embodiments,a prediction corresponds to one or more of a defined emergency type, adefined geographic area, or a defined time period. An example of such aprediction is 50 predicted emergency calls for medical emergencies inmunicipality ABC during a date XYZ. In some embodiments, the predictionmodel is based on a particular emergency type and makes predictionscorresponding to the emergency type. Alternatively, in some embodiments,the prediction model is not based on a particular emergency type andrepresents a generalized prediction model.

In some embodiments, an emergency type is a police emergency. In someembodiments, an emergency type is a fire emergency. In some embodiments,an emergency type is a medical emergency. In some embodiments, anemergency type is a vehicle emergency (e.g., a traffic accident). Insome embodiments, an emergency type includes more than one of a policeemergency, fire emergency, or medical emergency. In some embodiments, anemergency type includes more than one of a police emergency, a fireemergency, a medical emergency, or a vehicle emergency. In someembodiments, emergency data comprises one or more emergency typesdefined by an emergency dispatch center from which the emergency data isobtained. In some embodiments, an emergency type is a terrorismemergency, a natural disaster emergency, or an industrial accidentemergency. In some embodiments, an emergency type is defined forpurposes of obtaining a prediction for the defined emergency type.

In some embodiments, a geographic area comprises a city block, aneighborhood, a city, a county, a stretch of highway, a park, arecreation area, a sports stadium, a convention center, an area block,or other geographic area. In some embodiments, a geographic areacomprises an area block. In some embodiments, a geographic area is anarea block. In some embodiments, a geographic area is divided into alocational grid (“grid”) comprising one or more area blocks. In someembodiments, a geographic area comprises one or more area blocks. Insome embodiments, an area block is a square, a rectangle, a diamond, ahexagon, or some other geometric shape. In some embodiments, an areablocks inside a grid have the same shape and/or area. In someembodiments, area blocks inside a grid do not share the same shapeand/or area. In some embodiments, a geographic area is defined forpurposes of obtaining a prediction for the defined geographic area.

In some embodiments, an area block is no more than about 1, 2, 3, 4, 5,6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24,25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42,43, 44, 45, 46, 47, 48, 49, or 50 square kilometers. In someembodiments, an area block is at least about 1, 2, 3, 4, 5, 6, 7, 8, 9,10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27,28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45,46, 47, 48, 49, or 50 or more square kilometers. In some embodiments, anarea block is about 1 square kilometer to about 50 square kilometers. Insome embodiments, an area block is at least about 1 square kilometer. Insome embodiments, an area block is at most about 50 square kilometers.In some embodiments, an area block is about 1 square kilometer to about2 square kilometers, about 1 square kilometer to about 3 squarekilometers, about 1 square kilometer to about 4 square kilometers, about1 square kilometer to about 5 square kilometers, about 1 squarekilometer to about 10 square kilometers, about 1 square kilometer toabout 15 square kilometers, about 1 square kilometer to about 20 squarekilometers, about 1 square kilometer to about 25 square kilometers,about 1 square kilometer to about 30 square kilometers, about 1 squarekilometer to about 40 square kilometers, about 1 square kilometer toabout 50 square kilometers, about 2 square kilometers to about 3 squarekilometers, about 2 square kilometers to about 4 square kilometers,about 2 square kilometers to about 5 square kilometers, about 2 squarekilometers to about 10 square kilometers, about 2 square kilometers toabout 15 square kilometers, about 2 square kilometers to about 20 squarekilometers, about 2 square kilometers to about 25 square kilometers,about 2 square kilometers to about 30 square kilometers, about 2 squarekilometers to about 40 square kilometers, about 2 square kilometers toabout 50 square kilometers, about 3 square kilometers to about 4 squarekilometers, about 3 square kilometers to about 5 square kilometers,about 3 square kilometers to about 10 square kilometers, about 3 squarekilometers to about 15 square kilometers, about 3 square kilometers toabout 20 square kilometers, about 3 square kilometers to about 25 squarekilometers, about 3 square kilometers to about 30 square kilometers,about 3 square kilometers to about 40 square kilometers, about 3 squarekilometers to about 50 square kilometers, about 4 square kilometers toabout 5 square kilometers, about 4 square kilometers to about 10 squarekilometers, about 4 square kilometers to about 15 square kilometers,about 4 square kilometers to about 20 square kilometers, about 4 squarekilometers to about 25 square kilometers, about 4 square kilometers toabout 30 square kilometers, about 4 square kilometers to about 40 squarekilometers, about 4 square kilometers to about 50 square kilometers,about 5 square kilometers to about 10 square kilometers, about 5 squarekilometers to about 15 square kilometers, about 5 square kilometers toabout 20 square kilometers, about 5 square kilometers to about 25 squarekilometers, about 5 square kilometers to about 30 square kilometers,about 5 square kilometers to about 40 square kilometers, about 5 squarekilometers to about 50 square kilometers, about 10 square kilometers toabout 15 square kilometers, about 10 square kilometers to about 20square kilometers, about 10 square kilometers to about 25 squarekilometers, about 10 square kilometers to about 30 square kilometers,about 10 square kilometers to about 40 square kilometers, about 10square kilometers to about 50 square kilometers, about 15 squarekilometers to about 20 square kilometers, about 15 square kilometers toabout 25 square kilometers, about 15 square kilometers to about 30square kilometers, about 15 square kilometers to about 40 squarekilometers, about 15 square kilometers to about 50 square kilometers,about 20 square kilometers to about 25 square kilometers, about 20square kilometers to about 30 square kilometers, about 20 squarekilometers to about 40 square kilometers, about 20 square kilometers toabout 50 square kilometers, about 25 square kilometers to about 30square kilometers, about 25 square kilometers to about 40 squarekilometers, about 25 square kilometers to about 50 square kilometers,about 30 square kilometers to about 40 square kilometers, about 30square kilometers to about 50 square kilometers, or about 40 squarekilometers to about 50 square kilometers.

In some embodiments, an area block is no more than about 10, 50, 100,200, 300, 400, 500, 600, 700, 800, 900, or 1000 square meters, includingincrements therein. In some embodiments, an area block is at least about10, 50, 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 or moresquare meters, including increments therein. In some embodiments, anarea block is about 10 square meters to about 1,000 square meters. Insome embodiments, an area block is at least about 10 square meters. Insome embodiments, an area block is at most about 1,000 square meters. Insome embodiments, an area block is about 10 square meters to about 50square meters, about 10 square meters to about 100 square meters, about10 square meters to about 200 square meters, about 10 square meters toabout 300 square meters, about 10 square meters to about 400 squaremeters, about 10 square meters to about 500 square meters, about 10square meters to about 600 square meters, about 10 square meters toabout 700 square meters, about 10 square meters to about 800 squaremeters, about 10 square meters to about 900 square meters, about 10square meters to about 1,000 square meters, about 50 square meters toabout 100 square meters, about 50 square meters to about 200 squaremeters, about 50 square meters to about 300 square meters, about 50square meters to about 400 square meters, about 50 square meters toabout 500 square meters, about 50 square meters to about 600 squaremeters, about 50 square meters to about 700 square meters, about 50square meters to about 800 square meters, about 50 square meters toabout 900 square meters, about 50 square meters to about 1,000 squaremeters, about 100 square meters to about 200 square meters, about 100square meters to about 300 square meters, about 100 square meters toabout 400 square meters, about 100 square meters to about 500 squaremeters, about 100 square meters to about 600 square meters, about 100square meters to about 700 square meters, about 100 square meters toabout 800 square meters, about 100 square meters to about 900 squaremeters, about 100 square meters to about 1,000 square meters, about 200square meters to about 300 square meters, about 200 square meters toabout 400 square meters, about 200 square meters to about 500 squaremeters, about 200 square meters to about 600 square meters, about 200square meters to about 700 square meters, about 200 square meters toabout 800 square meters, about 200 square meters to about 900 squaremeters, about 200 square meters to about 1,000 square meters, about 300square meters to about 400 square meters, about 300 square meters toabout 500 square meters, about 300 square meters to about 600 squaremeters, about 300 square meters to about 700 square meters, about 300square meters to about 800 square meters, about 300 square meters toabout 900 square meters, about 300 square meters to about 1,000 squaremeters, about 400 square meters to about 500 square meters, about 400square meters to about 600 square meters, about 400 square meters toabout 700 square meters, about 400 square meters to about 800 squaremeters, about 400 square meters to about 900 square meters, about 400square meters to about 1,000 square meters, about 500 square meters toabout 600 square meters, about 500 square meters to about 700 squaremeters, about 500 square meters to about 800 square meters, about 500square meters to about 900 square meters, about 500 square meters toabout 1,000 square meters, about 600 square meters to about 700 squaremeters, about 600 square meters to about 800 square meters, about 600square meters to about 900 square meters, about 600 square meters toabout 1,000 square meters, about 700 square meters to about 800 squaremeters, about 700 square meters to about 900 square meters, about 700square meters to about 1,000 square meters, about 800 square meters toabout 900 square meters, about 800 square meters to about 1,000 squaremeters, or about 900 square meters to about 1,000 square meters.

In some embodiments, a time period is a time of the day, a day of theweek, a day of the month, a holiday, a duration of an environmentalevent (e.g., blizzard), or other time duration. In some embodiments, atime period is defined for purposes of obtaining a predictioncorresponding to the defined time period. In some embodiments, a timeperiod is regularly occurring, such as for example, a holiday thatoccurs once a year. In some embodiments, a regularly occurring definedtime period is a certain time of the day, such as for example, between 5PM and 7 PM during weekdays (e.g., rush hour). In some embodiments, atime period comprises at least one time block. In some cases, a 24 hourtime period is divided into a plurality of time blocks. In someembodiments, the plurality of time blocks comprise time blocks of equallength. Alternatively, in some embodiments, the plurality of time blockscomprise time blocks of unequal length. In some embodiments, a timeblock is at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,15, 16, 17, 18, 19, 20, 21, 22, 23, or 24 hours. In some embodiments, atime block is no more than about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, or 24 hours. In someembodiments, a time block is at least about 1, 2, 3, 4, 5, 6, 7, 8, 9,10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27,28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45,46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, or 60 minutes.In some embodiments, a time block is no more than about 1, 2, 3, 4, 5,6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24,25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42,43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, or60 minutes. In some embodiments, a time block is a specific subset of a24 hour time period. For example, in some embodiments, a time blockcomprises 12 AM-1 AM, 1 AM-2 AM, 2 AM-3 AM, 3 AM-4 AM, 4 AM-5 AM, 5 AM-6AM, 6 AM-7 AM, 7 AM-8 AM, 8 AM-9 AM, 9 AM-10 AM, 10 AM-11 AM, 11 AM-12PM, 12 PM-1 PM, 1 PM-2 PM, 2 PM-3 PM, 3 PM-4 PM, 4 PM-5 PM, 5 PM-6 PM, 6PM-7 PM, 7 PM-8 PM, 8 PM-9 PM, 9 PM-10 PM, 10 PM-11 PM, and 11 PM-12 AM,or any combination thereof. In some embodiments, a defined time periodcomprises a time of year. In some embodiments, a time period comprises aseason selected from summer, fall, winter, spring, or any combinationthereof. In some embodiments, a time period comprises one or more daysin the week. In some embodiments, a defined time period comprises atleast one day in the week selected from Monday, Tuesday, Wednesday,Thursday, Friday, or any combination thereof. In some embodiments, atime period comprises at least one day in the weekend selected fromSaturday and Sunday. In some embodiments, a time period comprises one ormore months of the year. In some embodiments, a defined time periodcomprises at least one month of the year selected from January,February, March, April, May, June, July, August, September, October,November, and December. In some embodiments, a time period comprises atleast one week of the year. In some embodiments, a time period comprisesat least one week of the year selected from week 1, 2, 3, 4, 5, 6, 7, 8,9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44,45, 46, 47, 48, 49, 50, 51, and 52. An illustrative example of a timeperiod comprises non-holiday Fridays in January and February between 5PM and 7 PM.

In some embodiments, at least one of emergency type, geographic area,and time period are defined. In some embodiments, a subject (e.g., auser) directly accesses an emergency prediction system, platform, ormedia to request a risk prediction by providing at least one of adefined emergency type, a defined geographic area, and a defined timeperiod. As an example, in some embodiments, a subject is a passenger ina motor vehicle on the freeway wishing to know the risk prediction foran accident for the next 50 mile stretch of freeway. In someembodiments, a subject is an emergency management system, a publicsafety answering point, an emergency dispatch center, an emergencyresponse personnel, or a user or administrator thereof. In someembodiments, at least one of a defined emergency type, a definedgeographic area, and a defined time period is pre-defined for automaticgeneration of at least one prediction model and/or one or more riskpredictions. For example, in some embodiments, an emergency managementsystem administrator (“admin”) wanting to obtain daily predictions foreach area block of a county for all police and fire related emergenciesis able to define the geographic area based on area blocks, define thetime period as a full day, and the emergency type as police and fireemergencies when requesting the daily emergency predictions.

Environmental conditions such as weather may have an impact on thenumber of emergencies within the geographic area during a specific timeperiod. Likewise, events such as a sports game may also have an effect.Weather conditions such as air temperature, wind speed, precipitation,fog, pavement temperature and condition, water level, and otherconditions may impact emergencies such as traffic accidents. Inaddition, various non-environmental events may have an impact on thenumber of emergencies. For example, Thanksgiving week is one of thedeadliest weeks of the year due to the spike in traffic accidents.Various factors may be responsible for large number of car crashesduring the week of Thanksgiving including the increased number ofvehicles on the roads, drivers navigating unfamiliar roads, driving inthe evening and/or under the influence. In addition to trafficaccidents, there are many medical emergencies associated withThanksgiving including knife wounds, burns, food poisoning,overconsumption, and more. In some embodiments, certain events such asfootball games, baseball games, basketball games, concerts and festivalsare associated with increase in certain types of emergencies. Forexample, sports events such as baseball games are associated withemergency rooms filling up with cases of alcohol poisoning, bodilytrauma, chest or stomach pain. The systems, methods, and media providedin the present disclosure as described herein organize and process thisemergency, environmental, and event data to generate prediction modelsthat quantify this relationship between emergencies and environmentalconditions and events in order to generate risk predictions.

In some embodiments, the systems, methods, and media described hereinare customizable by an admin, a user, an EMS, or an EDC to automaticallyprovide one or more predictions on a regular basis. In some embodiments,the systems, methods, and media described herein are customized toprovide automatic warnings specific to one or more subjects or users whoare not associated with the emergency response systems or personnel.

In some embodiments, the systems, methods, and media of the presentdisclosure enable an EMS or EDC to issue warnings or notifications ofelevated risk for specified emergencies based on risk predictions. Insome embodiments, the notifications are sent specifically to subjects orindividuals who are within the scope of the risk prediction (e.g.,located within the defined geographic area during the defined timeperiod). In some embodiments, notifications are sent to thecommunication devices of one or more subjects with the goal of providingpre-emptive warning to minimize any potential injury or damage that maybe caused by predicted emergency situations, and/or potentially preventthese situations from occurring at all. In some embodiments, anotification is sent automatically whenever a risk prediction exceeds adefined risk threshold. In some embodiments, a notification is sentautomatically whenever a risk prediction exceeds a defined riskthreshold by a minimum percentage. In some embodiments, a minimumpercentage is at least 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 60%,70%, 80%, 90%, 95%, or 99% or more, including increments therein. Insome embodiments, a defined risk threshold is an arbitrary value set byan administrator, user, EMC, EDC, or subject.

In some embodiments, a notification comprises information regarding anincreased probability of certain types of emergencies, their possibletime duration and geographic region of impact and possible methods for asubject to whom the warning is sent to mitigate impact of emergencysituations. In some embodiments, a notification is non-specific to anyone subject, but is sent to particular subject communication devicesbased on the location of the communication devices at the time of apredicted increase in probability of an emergency occurring at orproximate the location of the subject communication devices.

In some embodiments, a notification communicates information aboutchanges in traffic pattern on certain highways or roadways in a specificgeographic location and may further communicate suggestions foralternate routes to a subject through the communication device. Forexample, switching to one of the suggested alternate routes allows asubject to reduce the probability of a traffic based delay or othertraffic-based incident.

In some embodiments, the notification comprises information aboutweather-based events (e.g., heavy rain, thunderstorms, and snowstorms).In some embodiments a prediction model for predicting the probability ofoccurrence of emergency situations (e.g., emergencies/emergency calls)is trained using information provided to the prediction server aboutpublic events, for example, baseball games, basketball games, musicconcerts, and/or other public events in which a substantial number ofpeople are simultaneously hosted in one particular geographic location,and information regarding a history of requests for emergency assistanceplaced from the same geographic area as the public events, during,before, or after occurrence of the public events. In some embodiments, anotification is sent to subjects in a geographic location proximate atype of public event responsive to the prediction model indicating anincreased probability of an emergency event occurring in the geographiclocation and resulting from or correlated with occurrences of the typeof public event.

Regression Analysis

In some embodiments, regression analysis is used to generate predictionssuch as, for example, estimated response time for an emergencycommunication. Regression analysis is a class of modeling techniquesthat uses data to establish a mathematical relationship betweendifferent variables (features). Regression analysis includes techniquesthat usually focus on understanding how the values of a “dependent orresponse” variable change when one or more “independent or predictor”variables change. In some embodiments, regression analysis is used todetermine a dependent variable such as estimated response time based onpredictor variables obtained from emergency data. In some embodiments,predictor variables include one or more of emergency type, emergencytime, emergency location, environment data, event data, emergencyresource data (e.g., type, location, and/or availability of emergencyresources), or response time data.

In some embodiments, regression analysis is linear regression. In linearregression, the dependent variable is a numerical outcome. Predictionsfor the dependent variable are modeled using a linear combination of thepredictor variables (or transformed predictor variables). The unknownmodel parameters are then estimated from the data. In some embodiments,linear regression analysis is used when at least one of the followingassumptions is made: 1) the predictor variables should be in a linearrelationship with the numerical dependent variable; 2) errors(residuals) should be independent and normally distributed; 3) thereshould be no or little multicollinearity (the predictor variables shouldnot be in a linear relationship among themselves); 4) the variance ofthe errors (residuals) should be approximately the same across alllevels of the predictor functions (homoscedasticity). In someembodiments, data for the predictor variables and the data for thedependent variable are the inputs of the model. In some embodiments, theoutput is a linear equation, fitted via Ordinary Least Squares (OLS),relating the predictor and response variables (or functions).

In some embodiments, when one or more dependent variables have discreterather than continuous values, discrete choice models are used insteadof multiple linear regression such as, for example, logistic regression,multinomial logistic regression, or probit regression.

In some embodiments, regression analysis is logistic regression. Inlogistic regression, the dependent variable is a binary outcome (e.g.,yes/no, usually coded as I/O). A logistic model is used to estimate theprobability of the binary outcomes (instead of the exact outcome) basedon one or more predictor variables. In some embodiments, for example,logistic regression allows a determination as to whether the presence ofa feature (variable) increases the probability of a given outcome by aspecific percentage. In some embodiments, a logarithm of the odds-ratio(which is the probability of the outcome of interest divided by theprobability of the other outcome) is modeled as a linear combination ofthe predictor variables. In some embodiments, logistic regression isused when at least one of the following assumptions is made: 1) thepredictor variables should be in a linear relationship with thelogarithm of the odds ratio; 2) errors (residuals) should be independentand follow a logistic distribution; 3) there should be littlemulticollinearity (the predictor variables should not be in a linearrelationship among themselves). In some embodiments, data for thedependent variables and the data for the predictor variables are theinputs of the model. In some embodiments, the output is a linearequation, fitted via Maximum Likelihood Estimation (MLE), relating thepredictor variables and the logarithm of the odds-ratio. In someembodiments, multinomial and/or ordinal logistic regression is used forregression analysis when the dependent variables have more than twooutcomes. In some embodiments, probit regression is used for regressionanalysis. Probit regression is similar to logistic regression, with amain difference being that the error term follows a logisticdistribution with mean zero in the logistic model, whereas the errorfollows a normal distribution with mean zero in the probit model.

In some embodiments, a non-regression based analysis is used in thesystems, methods, and media described herein. An example of anon-regression based analysis is time series analysis. Time seriesanalysis comprises methods for analyzing time series data in order toextract meaningful statistics and other characteristics of the data.Time series forecasting uses a model to predict future values based onpreviously observed values.

In some embodiments, classification and regression trees (CART) are usedto identify the statistical model that has maximum accuracy forpredicting the value of a categorical dependent variable for a datasetconsisting of categorical and continuous variables. In some embodiments,multivariate adaptive regression splines (MARS) is used to build a modelstepwise linear regressions.

Machine Learning Algorithms

In some embodiments, the systems, methods, and media described hereinuse machine learning algorithms for training prediction models and/ormaking predictions. Machine learning explores the study and constructionof algorithms that are capable of learning from and making predictionson data. In some embodiments, techniques used for generating modelsand/or making predictions include machine learning, neural networks,multilayer perceptron (MLP), support vector machines (SVM), radial basisfunction, Naïve Bayes, nearest neighbor, or geospatial predictivemodeling.

In some embodiments, a machine learning algorithm uses a supervisedlearning approach. In supervised learning, the algorithm generates afunction from labeled training data. Each training example is a pairconsisting of an input object and a desired output value. In someembodiments, an optimal scenario allows for the algorithm to correctlydetermine the class labels for unseen instances. In some embodiments, asupervised learning algorithm requires the user to determine one or morecontrol parameters. These parameters are optionally adjusted byoptimizing performance on a subset, called a validation set, of thetraining set. After parameter adjustment and learning, the performanceof the resulting function is optionally measured on a test set that isseparate from the training set. Regression methods are commonly used insupervised learning.

In some embodiments, a machine learning algorithm uses an unsupervisedlearning approach. In unsupervised learning, the algorithm generates afunction to describe hidden structures from unlabeled data (e.g., aclassification or categorization is not included in the observations).Since the examples given to the learner are unlabeled, there is noevaluation of the accuracy of the structure that is output by therelevant algorithm. Approaches to unsupervised learning include:clustering, anomaly detection, and neural networks.

In some embodiments, a machine learning algorithm uses a semi-supervisedlearning approach. Semi-supervised learning combines both labeled andunlabeled data to generate an appropriate function or classifier.Semi-supervised learning is usually used in data augmentation.

In some embodiments, a machine learning algorithm uses a reinforcementlearning approach. In reinforcement learning, the algorithm learns apolicy of how to act given an observation of the world. Every action hassome impact in the environment, and the environment provides feedbackthat guides the learning algorithm.

In some embodiments, a machine learning algorithm uses a transductionapproach. Transduction is similar to supervised learning, but does notexplicitly construct a function. Instead, tries to predict new outputsbased on training inputs, training outputs, and new inputs.

In some embodiments, a machine learning algorithm uses a “learning tolearn” approach. In learning to learn, the algorithm learns its owninductive bias based on previous experience.

In some embodiments, a machine learning algorithm is applied to new orupdated emergency data to be re-trained to generate a new predictionmodel. In some embodiments, a machine learning algorithm or model isre-trained periodically. In some embodiments, a machine learningalgorithm or model is re-trained non-periodically. In some embodiments,a machine learning algorithm or model is re-trained at least once a day,a week, a month, or a year or more. In some embodiments, a machinelearning algorithm or model is re-trained at least once every 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22,23, 24, 25, 26, 27, 28, 29, or 30 days or more.

Spatiotemporal Prediction Methods

The systems, methods, and media described herein provide for thegeneration of spatiotemporal prediction models and/or algorithms formaking predictions. Spatiotemporal models are possible when data iscollected across time and space. In some embodiments, data inputs forthe spatiotemporal model comprise a two dimensional location set(discrete or continuous) and a one-dimensional temporal set (discrete orcontinuous). For a continuous location model, in some embodiments, theoutput is a set of density estimates for each time value.

In some embodiments, spatiotemporal prediction models utilize a discretelocation approach. Discrete location spatial modeling restrictsattention to a finite set of known fixed locations, effectivelydiscretizing space and turning the problem into a multivariate dataanalysis problem where each location represents a single dimension.

In some embodiments, spatiotemporal prediction models utilize acontinuous location approach. For continuous models, the approach is toexplore general distributional patterns. A simple approach to modelingspatial density is to use a single Gaussian density function. In someembodiments, a finite mixture of Gaussian densities provides additionalflexibility (Gaussian mixture models). While mixture models can provideadditional modeling power beyond that of a single Gaussian, it has anumber of practical limitations. In some embodiments, a kernel densityestimation (KDE) method is used for spatiotemporal prediction modeling.Kernel density estimation is a non-parametric method for estimating adensity function from a random sample of data.

In some embodiments, spatiotemporal prediction models utilize a discreteand continuous time approach. In discrete and continuous timespatiotemporal models, time is considered either a discrete orcontinuous variable, with most methods for discrete time havingextensions to continuous time.

Data Augmentation

In some embodiments, the systems, methods, and media described hereinprovide for data augmentation. In some embodiments, data augmentationcomprises predicting labels for unlabeled emergency data andincorporating or adding the predicted labels to the unlabeled emergencydata to generate augmented emergency data. Fragmentation of emergencyservices often results in variation in the type of emergency dataavailable in different areas. Moreover, the combination of public andprivate emergency service providers often leads to distinct pools ofemergency data in the same emergency jurisdiction (e.g., a county orcity). The lack of standardization and/or harmonization of these datapools pose a challenge to generating effective models. Consequently, thedata pools have to be modeled separately, which often results in a lossof predictive power if a given data pool lacks sufficient data togenerate a robust model. Moreover, in some instances, a data pool thatis unlabeled cannot be used for certain models for generatingpredictions based on data labels. Accordingly, data augmentation allowsunlabeled data to be enhanced with label predictions, thereby expandingthe pool of available “labeled” data for further analysis and/ormodeling. For example, in some embodiments, labeled data is matched withunlabeled data to generate matched data. In some embodiments, thematched data is used to train a prediction algorithm. The predictionalgorithm

In some embodiments, data augmentation comprises supplementing oraugmenting emergency data with additional data/information such asenvironment data, event data, subject data, or any combination thereof.In some embodiments, augmented data does not comprise environmentaldata, event data, subject data, or any combination thereof. In someembodiments, additional data 232 forms an input into the system 200 asshown in FIG. 2. In some embodiments, additional data 232 is obtainedfrom one or more public or privately available sources. In someembodiments, additional data 232 comprises environmental data. In someembodiments, environmental data is comprises information on trafficcondition, weather condition, road condition, or any combinationthereof. In some embodiments, additional data 232 comprises informationabout demographics, population density, etc., regarding the area, whichis useful for making emergency prediction(s). In some embodiments,additional data 232 includes information about geographical orjurisdictional boundaries for PSAPs, counties, towns, cities, or othergovernment borders. In some embodiments, additional data 232 includesinformation on a concert, sporting event, political demonstration,festival,

In some embodiments, data augmentation utilizes heuristic extrapolation,wherein the relevant fields are updated or provided with extrapolatedvalues. In some embodiments, data augmentation utilizes tagging, whereincommon records are tagged to a group, making it easier to understand anddifferentiate for the group. In some embodiments, data augmentationutilizes aggregation, wherein values are estimated using means or othersummaries for relevant fields. In some embodiments, data augmentationutilizes heuristic or analytical probabilities to populate values basedon the probability of events. In some embodiments, emergency data isaugmented by adding or incorporating additional data such as data thatis likely to be relevant to an emergency. Examples of data likely to berelevant to an emergency include environmental data, event data, subjectdata, or any combination thereof. In some embodiments, augmentingemergency data comprises associating the emergency data with additionaldata using at least one of emergency type, geographic area (or locationcoordinates), and time period. For example, a subset of emergency datafor traffic accidents is augmented with weather information that is inproximity to the geographic area and time period corresponding to thesubset of emergency data. As a result, emergency data comprising trafficaccident (emergency type), geographic area (location of accident), andtime period (time of emergency communication relating to accident) isaugmented to include weather conditions such as black ice on the road atthe geographic location during the time period.

Emergency Anomaly or Event Detection

In some embodiments, the systems, methods, and media described hereinprovide for detection of emergency anomalies or events in real-time ornear real-time. In some embodiments, emergency anomaly detectioncomprises the identification of items, events, or observations which donot conform to an expected pattern or other items in a dataset.

In some embodiments, an unsupervised anomaly detection technique is usedto detect anomalies in an unlabeled test data set. In some embodiments,unsupervised anomaly detection looks for instances that seem to fitleast to the remainder of the data set under the assumption that themajority of the instances in the data set are normal.

In some embodiments, a supervised anomaly detection technique is used todetect anomalies in a data set that has been labeled as “normal” and“abnormal” and involves training a classifier. A key difference comparedto most other statistical classification problems is the inherentunbalanced nature of outlier detection.

In some embodiments, a semi-supervised anomaly detection technique isused to construct a model representing normal behavior from a givennormal training data set, and then testing the likelihood of a testinstance to be generated by the trained model.

In some embodiments, a density-based technique or cluster analysis isused for anomaly detection.

In some embodiments, an emergency anomaly is a large-scale disaster,accident, or attack. As referenced herein, “large-scale” refers to anevent affecting more than a threshold number of people, animals,property, or any combination thereof. For example, in some embodiments,an event is large-scale if it affects more than 5, 10, 20, 30, 40, 50,100, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 or more, includingincrements therein, people, animals, properties, or any combinationthereof. Examples of emergency anomalies include, but are not limitedto, an earthquake, landslide, tsunami, volcanic activity, wildfire,large-scale fire, cyclone, tornado, hurricane, epidemic, extremetemperature, industrial accident, chemical spill, nuclear accident,terrorist attack, plane crash, oil spill, train derailment, snowstorm,thunderstorm, or large-scale transport accident.

In some embodiments, emergency anomaly detection is carried out for alocation of a first member device belonging to a group of devicesauthorized to share data. In further embodiments, a notification of adetected emergency anomaly is sent to a second member device in thegroup of devices. In some embodiments, emergency anomaly detection iscarried out for a first member device belonging to a group of devicesbased on information provided by a group of devices. In furtherembodiments, a proxy call is initiated on behalf of the first memberdevice when an emergency anomaly is detected for the member device. Inyet further embodiments, the proxy call is an emergency call to at leastone of an emergency management system and an emergency dispatch center.In further embodiments, a proxy call is initiated on behalf of the firstmember device when an emergency anomaly is detected at a location of thefirst member device. In further embodiments, a proxy call is initiatedon behalf of the first member device by a second member device in thegroup of devices when an emergency anomaly is detected for the firstmember device. In yet further embodiments, a location of the firstmember device is provided to a recipient of the proxy call. In yetfurther embodiments, the emergency data is provided to a recipient ofthe proxy call. In some embodiments, the emergency data is obtained froma group of devices comprising member devices authorized to share data.In some embodiments, the emergency prediction system executes theemergency anomaly detection algorithm in response to receiving a requestto detect an emergency anomaly from a communications device. In someembodiments, the emergency prediction system executes the emergencyanomaly detection algorithm in response to receiving a request to detectan emergency anomaly from a member device in a group of devicesauthorized to share data.

Resource Allocation

In some embodiments, the systems, methods, and media described hereinprovide for enhanced resource allocation. In some embodiments, resourceallocation is optimized based on spatiotemporal prediction(s) andemergency resource data. In some embodiments, resource allocation isoptimized using a prediction and simulation process. In someembodiments, resource allocation is optimized with the goal ofminimizing emergency response times for a given set of emergencyresources. In some embodiments, emergency response times are minimizedwhile also minimizing operational costs or keeping operational costs low(e.g., below a certain cost threshold set by an administrator).

In some embodiments, resource allocation optimization comprises using agreedy algorithm. A greedy algorithm follows the problem solvingheuristic of making the locally optimal choice at each stage with thehope of finding a global optimum. In some cases, a greedy strategy doesnot in general produce the optimal solution, but instead generateslocally optimal solutions that approximate a global optimal solution ina reasonable time.

Data Summary

In some embodiments, data is emergency data. In some embodiments,emergency data comprises data for emergency communications such as, forexample, emergency time (e.g., time of emergency call and/or callduration), emergency location (e.g., location of caller or callingdevice), emergency type (e.g., fire, police, or medical emergency), orany combination thereof. In some embodiments, emergency data isproprietary emergency data. In some embodiments, proprietary emergencydata is unlabeled emergency data. In some embodiments, emergency data isunlabeled emergency data. Unlabeled emergency data has limitedinformation such as, for example, emergency time and emergency locationwhile lacking labels such as emergency type and/or emergency priority.In some embodiments, emergency data is labeled emergency data. Labeledemergency data has label information for emergency communications suchas, for example, emergency type and/or emergency priority (e.g., low orhigh call priority). In some embodiments, labeled emergency data isobtained from at least one emergency dispatch center as historical datasince current labeled emergency data is typically not available outsideof the emergency dispatch center.

In some embodiments, data comprises electronic information stored on aserver. In some embodiments, data comprises information obtained fromcommunication devices such as, for example, a landline phone. In someembodiments, data comprises information obtained from wireless mobiledevices such as, for example, a smart phone. In some embodiments, datacomprises information stored in a database. In some embodiments, datacomprises information for environmental conditions (“environment data”)such as, for example, precipitation level or temperature. In someembodiments, data comprises information on events (“event data”) suchas, for example, the date of a holiday. In some embodiments, datacomprises information on emergencies or emergency requests (“emergencydata”) such as, for example, the number of emergency calls, emergencyrequests, and/or emergency incidents. In some embodiments, datacomprises historical data comprising information on past data. Forexample, in some embodiments, historical data comprises the emergencytype, emergency location, and emergency time of one or more emergenciesthat has already taken place, and not an ongoing emergency or apredicted future emergency. In some embodiments, historical datacomprises data that is at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48,49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60 minutes old or more. Insome embodiments, historical data is data that is at least 1, 2, 3, 4,5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23,24 hours old or more. In some embodiments, historical data is data thatis at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 days old or more. Insome embodiments, historical data is data that is at least 1, 2, 3, 4,5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23,24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41,42, 43, 44, 45, 46, 47, 48, 49, 50, 51, or 52 weeks old or more. In someembodiments, current data comprises current emergency data. For example,in some embodiments, current emergency data comprises at least one ofemergency type, emergency time, and emergency location for an ongoingemergency/emergency communication. In some embodiments, current data isno more than 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,54, 55, 56, 57, 58, 59, or 60 minutes old. In some embodiments, currentdata is data that is no more than 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, or 24 hours old. In someembodiments, current data is no more than 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,29, 30 days old. In some embodiments, data comprises future datacomprising information on future environmental conditions, events,emergencies, or any combination thereof. In some embodiments, futuredata is data on environmental conditions, events, or emergencies thathave not yet occurred or are not yet in existence. For example, in someembodiments, future data comprises information on planned events such asa planned parade including the event type, event time (e.g., date, timeof day, and/or duration), and event location.

In some embodiments, unlabeled emergency data is augmented by merging,combining, or incorporating label information from labeled emergencydata. Augmented emergency data comprises data from both unlabeledemergency data and labeled emergency data. In some embodiments,unlabeled or labeled emergency data is augmented with additionalinformation such as, for example, environment data and/or event data. Insome embodiments, environment data and/or event data corresponding toemergency data is identified based on emergency time and emergencylocation. For example, environment data such as weather is capable ofbeing identified based on a specific time and location. In someembodiments, augmented emergency data comprises emergency type,emergency time, emergency location, emergency priority, environmentdata, event data, or any combination thereof.

Emergency data refers to information about emergencies that haveoccurred or are on-going and optionally includes the type of emergency(such as medical, fire, police or car crashes), the location of theemergency (e.g., GPS coordinates, altitude, etc.), the time of theemergency (e.g., date and time), or any combination thereof. In someembodiments, additional information regarding the emergency is obtainedincluding, but not limited to, fatalities, types of injuries, proximityto landmarks (such as sports stadiums), signal strength for emergencycall, whether the subject was in a vehicle during the emergency,information about road conditions, number and effectiveness of emergencyservice providers involved, time for emergency response, etc. Emergencydata may comprise historical data or current data.

In some embodiments, the emergency type is selected from vehicle/trafficemergency, fire emergency, police emergency, medical emergency, or anycombination thereof. In some embodiments, a vehicle emergency is a flattire, collision with another vehicle, collision with a wall orartificial barrier, collision with a tree or natural barrier, collisionwith a pedestrian, collision with a cyclist, collision with amotorcyclist, collision with a wild animal, collision with adomesticated animal, collision with a pet, rollover, or running off theroad. In some embodiments, a medical emergency is a heart attack,cardiac arrest, stroke, seizure, anaphylactic shock, electrical shock,cut, abrasion, contusion, stab wound, gunshot wound, broken bone,poisoning, burn, bug bite or sting, snake bite, animal attack,concussion, dismemberment, drowning, death, or any combination thereof.In some embodiments, a police emergency type is robbery, armed robbery,attempted robbery, home invasion, battery, rape, arson, kidnapping,shooting, terrorist attack, or any combination thereof. In someembodiments, a fire emergency type is a home fire, building fire,wildfire, chemical spill, explosion, electrical fire, chemical fire,combustible metal fire, flammable liquids fire, solid combustibles fire,or any combination thereof.

In some cases, the emergency data comprises an emergency call log withbasic information such as a timestamp, GPS coordinates, and type ofemergency (e.g., as indicated by the subject). In other embodiments, theemergency data is the content of multi-media alerts sent by the subjectsto the EMS within a time period. In some embodiments, the emergency datais the content of the emergency session with the EDC including detailsregarding the emergency.

In some embodiments, emergency data is sourced from one or more EMS thatreceives, routes, monitors, or bridges emergency calls. In someembodiments, the EMS serves as a conference bridge for emergency alertsand calls from subjects with emergency dispatch center(s) such as PSAPs.In addition, the emergency data is obtained from publicly available dataabout emergencies.

Environment data comprises information about one or more environmentalconditions. For example, environmental conditions include temperature,precipitation (e.g., snow, hail, rain, sleet, etc.), thunderstorms,pressure, wind speed and/or direction, cloud conditions, extreme weather(such as tornadoes, high winds, hurricanes, frigid conditions etc.),earthquakes, wildfires, and more. In some embodiments, the environmentdata comprises road conditions (such as pavement temperature, black iceon bridges, road grip, curvature, obstructions, etc.) and traffic data(such as traffic density, direction of traffic, accidents, etc.). Insome embodiments, environment data is stratified into two or morecategories. For example, in some embodiments, temperature is stratifiedinto cold, warm, and hot categories. In some embodiments, a coldtemperature category is less than about 25, 24, 23, 22, 21, 20, 19, 18,17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, −1, −2,−3, −4, −5, −6, −7, −8, −9, −10, −11, −12, −13, −14, −15, −16, −17, −18,−19, −20, −21, −22, −23, −24, or −25 degrees Celsius or lower. In someembodiments, a warm temperature category is between about 10-15, 15-20,20-25, 25-30, 30-35 degrees Celsius, or any combination thereof. In someembodiments, a hot temperature category is at least about 20, 21, 22,23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 45, 35, 36, 37, 38, 39, 40,41, 42, 43, 44, or 45 degrees Celsius or higher.

In some embodiments, environment data is stratified into two categoriesindicating the presence or absence of an environmental condition. Forexample, temperature may be stratified into freezing and non-freezingtemperature categories with freezing temperatures comprisingtemperatures at or below zero degrees Celsius, and non-freezingtemperatures comprising temperatures above zero degrees Celsius. In someembodiments, environment data comprise environment type, environmentlocation, and environment time for one or more environmental conditions.In some embodiments, the systems, methods, and media described hereinprocess the environment data to obtain data pertaining to environmenttype, environment location, and environment time. In some embodiments,environment data values are matched to emergency data or emergency datastream(s) based on time and location. In some embodiments, environmentdata is sourced from one or more publicly accessible or private serversor databases. For example, climate data online is accessible viaNational Centers for Environmental Information for global historicalweather or climate data.

Event data refers to information on one or more public or private eventsor holidays such as, for example, Thanksgiving or Christmas. In someembodiments, event data comprises information on event type, eventlocation, and event time for one or more events. In some embodiments,event data includes information on a variety of event types such as, forexample, festivals, concerts, public gatherings, sports events or games,conferences, workshops, conventions, and other events. In someembodiments, sports events are amateur or professional events. In someembodiments, the event is a recurring event such as, for example,Thanksgiving, Halloween, or the day of the week (e.g., Monday). Holidaysmay include national holidays. In some embodiments, the event may beman-made such as a holiday, Day Light Savings Time change, politicalelections, or other man-made events.

In some embodiments, event data is sourced from one or more private orpublic calendars. For example, some states, municipalities, publicorganizations have publicly available calendars such as the CaliforniaData Portal. Many private organizations have a schedule of events ontheir website or in brochures and promotional materials such as theChicago Cubs organization.

Subject data refers to data from subjects, which is optionally obtainedvia their communication devices (such as mobile phones, wearabledevices, etc.). In some embodiments, subject data includes historical orcurrent locational information (e.g., GPS information or location withina building, etc.). In some embodiments, subject data may includesubject's travel information including speed and direction of travel. Insome embodiments, subject data comprises subject location such as, forexample, GPS coordinates, altitude, direction of travel, speed oftravel, mode of transportation (e.g., by car, plane, train, on foot,etc.), or any combination thereof

Predictive Analytics

FIG. 1 illustrates exemplary system components for an emergencyprediction system 100. As shown in FIG. 1, several input data 112, 122,132 enter the system 100 and output services 190 are generated in thebatch layer 150 or a real-time layer 160. The call data stream 112 is areal-time stream of data of incoming emergency calls. In someembodiments, the call data stream 112 is a proprietary call data streamor a third-party call data stream (e.g., a third-party emergency callhandler system or a data stream from a PSAP).

In some embodiments, the data stream 112 is obtained from a mobileapplication installed on a subject device. In some embodiments, when asubject requests emergency assistance or an emergency is detectedautomatically, an alert is sent from the subject device to an EmergencyManagement System (EMS) (not shown). In some embodiments, the system 100is a component of the EMS. In other embodiments, the system 100 is aseparate system that receives the data stream from the EMS, wherein theEMS or an Emergency Dispatch Center (EDC) (e.g., a PSAP) accesses anoutput or prediction via output services 190. Other embodiments withouta mobile application are also contemplated.

In some embodiments, the data stream 112 indicates when an emergencycall has been made with the time and location of the device. In someembodiments, each call entry in the data stream includes a time stamp(e.g., emergency time) and the location (e.g., latitude, longitude,elevation, physical address, floor or suite number, etc.). In someembodiments, the data stream includes additional information such asphone number, user name, saved address information, account information,demographics, medical information, etc. In some embodiments, the datastream 112 includes subject data. An exemplary data stream 112 isdepicted in Table 1.

Emergency data 122 is one form of input provided to the system 100. Insome embodiments, emergency data 122 is historical emergency call datafrom a state or region. In some embodiments, the emergency data 122includes proprietary emergency data. In some embodiments, proprietaryemergency data is sent from or originates from subject devices and isobtained from one or more databases in the EMS or other remote servers.In some embodiments, the proprietary emergency data is generated whenreal-time call data from the data stream 112 are saved in the MasterDatabase 154. In some embodiments, the proprietary emergency data 124 iswarehoused and updated in near real-time or on a periodic basis (hourly,weekly, etc.).

In some embodiments, the emergency data 122 is available emergency dataobtained from publicly or privately available sources. For example,police calls in Seattle are available online(https://catalog.data.gov/dataset/seattle-real-time-fire-911-calls-6cdf3).In some embodiments, the emergency data 122 is obtained from partners orcustomers (such as EDCs, PSAPs, municipalities, private dispatchcenters, etc.).

In some embodiments, the emergency data 122 is labeled emergency data.Labeled emergency data refers to emergency data that includes one ormore labels for characterizing the type or nature (e.g., emergencytype), priority (e.g., emergency priority), emergency response (e.g.,emergency response time), or other labels relevant to an emergencysituation. For example, in some embodiments, an emergency type includesfire, police, medical, car/traffic accident, pet rescue, water rescue,earthquake, avalanche, tsunami, or some other type of emergency,including those described elsewhere herein. In some embodiments, thepriority for the emergency includes one or more levels for priority suchas high, medium, low, or other priority level. In some embodiments, thelabeled emergency data includes the actual response time for thatparticular emergency. Exemplary labeled data, which is optionallydepicted as a data stream with predicted labels or labeled emergencydata when available in real-time, is depicted in Tables 4, 5, and 6.

TABLE 5 Sample Labeled Emergency Data Event Incident_LocationInitial_Emergency_Type Initial_Emergency_Subtype At_Scene_Time 1(47.62083, Theft Other Property Jan. 4, 2017 −122.32594) 19:00 2(47.613758, Theft Other Property Jan. 4, 2017 −122.34716) 18:46 3(47.620354, Residential Burglaries Burglary Jan. 4, 2017 −122.306046)15:59 4 (47.56021, Traffic Related Calls Motor Vehicle Collision Jan. 4,2017 −122.38548) Investigation 14:04 5 (47.634743, Parking ViolationsTraffic Related Calls Mar. 16, 2017 −122.356285) 10:30 6 (47.524677,Crisis Call Behavioral Health Apr. 1, 2017 −122.365875) 11:34 7(47.613235, Suspicious Circumstances Lewd Conduct Apr. 18, 2017−122.32947) 19:31 8 (47.71957, Narcotics Complaints Narcotics ComplaintsApr. 1, 2017 −122.337685) 14:54 9 (47.625435, Commercial BurglariesBurglary Apr. 1, 2017 −122.35083) 12:13 10 (47.690567, Threats,Harassment Threats, Harassment Jan. 4, 2017 −122.34586) 17:34

TABLE 6 Sample Labeled Emergency Data Uid Call Time Lat Lon TypePriority At Scene Time e1b9f2 May 3, 2017 0:00 33.12 −117.08 Fire HighMay 3, 17 0:17:20 e1b9f2 May 3, 2017 1:03 33.12 −117.08 Medical Med May3, 17 1:07:08 dc3b48 May 3, 2017 5:27 33.12 −117.08 Fire Med May 3, 175:39:03 8f0b19 May 3, 2017 13:39 34.02 −118.43 Police Low May 3, 1713:51:06 6d9427 May 3, 2017 16:49 29.76 −95.37 Fire Med May 3, 1716:57:34 2087ce May 3, 2017 22:33 26.22 −98.31 Medical High May 3, 1722:39:04 d35b93 May 3, 2017 23:11 42.58 −84.85 Medical Med May 3, 1723:22:44 027a9e May 4, 2017 19:50 26.11 −81.69 Fire High May 4, 1719:56:52 67c8c0 May 4, 2017 20:00 35.56 −82.64 Fire Low May 4, 1720:03:38 8f5ef2 May 4, 2017 20:24 31.68 −83.27 Fire High May 4, 1720:35:20

Examples of unlabeled data are provided in Table 7.

TABLE 7 Sample Unlabeled Emergency Data Call Time Priority DistrictDescription Call Number Incident Location Location Jul. 13, 2015 Med CDSee Text P151941002 0 N Calvert St (39.2899299,− 10:41 76.6123462) Jul.13, 2015 Med CD 911/No P151941003 600 E Fayette St (39.2906737,− 10:47Voice 76.6071600) Jul. 13, 2015 Med CD 911/No P151941004 200 E Baltimore(39.2898910,− 10:42 Voice St 76.6120720) Jul. 13, 2015 Low CD PrkgP151941005 800 Park Av (39.2985163,− 10:45 Complaint 76.6184754) Jul.13, 2015 Med SW Auto Theft P151941006 3500 Clifton Av (39.3112130,−10:46 76.6763150) Jul. 13, 2015 Med ND Family P151941007 2700 N CalvertSt (39.3208510,− 10:47 Disturb 76.6147390) Jul. 13, 2015 High WD SilentP151941008 2100 W North Av (39.3097096,− 10:46 Alarm 76.6513109) Jul.13, 2015 Low SW Auto P151941010 3100 Wilkens Av (39.2756929,− 10:49Accident 76.6664179) Jul. 13, 2015 Med NE Family P151941011 4800 GilrayDr (39.3483090,− 10:48 Disturb 76.5768440) Jul. 13, 2015 Med NDNarcotics P151941012 W Garrison (39.349653,− 10:49 Av/Pimlico Rd76.669145)

Another input into the system 100 is additional data 132 obtained fromone or more public or privately available sources. In some embodiments,additional data 132 is incorporated into emergency data. In someembodiments, emergency data comprises additional data 132. In someembodiments, emergency data is augmented by adding or incorporatingadditional data 132. In some embodiments, additional data 132 includesenvironmental data. In some embodiments, environmental data includes oneor more of traffic condition, weather condition, and road condition.

In some embodiments, environmental data comprises information about oneor more environmental conditions. For example, environmental conditionsinclude temperature, precipitation (e.g., snow, hail, rain, sleet,etc.), thunderstorms, pressure, wind speed and/or direction, cloudconditions, extreme weather (such as tornadoes, high winds, hurricanes,frigid conditions etc.), earthquakes, wildfires, and more. In someembodiments, the environmental data comprises road conditions (such aspavement temperature, black ice on bridges, road grip, curvature,obstructions, etc.) and traffic data (such as traffic density, directionof traffic, accidents, etc.).

In some embodiments, weather conditions such as air temperature, windspeed, precipitation, fog, pavement temperature and condition, waterlevel, and other conditions impact emergencies such as traffic accidentsand cause a delay in emergency response. In some embodiments, weatherinformation is obtained from third-party APIs provided by companies suchas the Weather Channel Service(https://twcservice.mybluemix.net/rest-api/). In some embodiments,traffic information is obtained from third-party APIs. Non-limitingexamples of third-party APIs include Google Waze and MapQuest.

In some embodiments, additional data 132 includes information aboutdemographics, population density, and other information regarding thearea. In some embodiments, additional data 132 is used for emergencyprediction. Exemplary sources for additional data 132 include census andsurveys, such as data provided by the U.S. Census Bureau using APIs(population estimates API:https://www.census.gov/data/developers/data-sets/popest-popproj/popest.html;map API: https://tigerweb.geo.census.gov/arcgis/rest/services/TIGERweb).

In some embodiments, additional data 132 comprises information aboutgeographical or jurisdictional boundaries for PSAPs, counties, towns,cities, municipalities, military bases, or other governmentaljurisdictions. In some embodiments, additional data 132 comprises zoninginformation and/or other boundaries (such as boundaries of a NationalPark, private commercial area, etc.). This information is generallystatic and is often helpful in predicting emergency response time. Insome embodiments, visualizations of the emergency prediction areprovided to a user or administrator (e.g., used to generate a virtualmap for displaying emergency predictions, emergency resourceallocations, and/or emergency anomalies). In some embodiments, thevisualization is a spatiotemporal emergency prediction visualization. Insome embodiments, a spatiotemporal emergency prediction visualizationprovides a visual representation of the prediction such as, for example,a density map of emergency events, emergency communications, emergencysignals, or any combination thereof.

For some emergency predictions, geographical or jurisdictional boundarydata is primarily used for filtering initial model inputs and enhancingoutputs with geographic visualization. Also, in some embodiments, theinput data is constrained to a city, census tract, or PSAP area level inthe pre-processing steps by filtering the data using geographical orjurisdictional boundaries.

In some embodiments, additional data 132 comprises event data, whichprovides information regarding events such as, for example, the date ofa holiday. In some events, event data comprises information about thedate and location of a large gathering, such as a sporting event,concert, etc. Event data refers to information on one or more public orprivate events or holidays such as Thanksgiving or Christmas. In someembodiments, event data comprises information on event type, eventlocation, and event time for one or more events. In some embodiments,event data includes information on a variety of event types such as, forexample, festivals, concerts, public gatherings, sports events or games,conferences, workshops, conventions, and other events. In someembodiments, event data is sourced from one or more private or publiccalendars. For example, some states, municipalities, and publicorganizations have publicly available calendars such as the CaliforniaData Portal. Many private organizations have a schedule of events ontheir website or in brochures and promotional materials such as theChicago Cubs organization. In some embodiments, when specificinformation about the event is not available, e.g., the time of theevent, a forecast for the time is set based on previous such events.

In some embodiments, a model requires additional information to makeaccurate emergency predictions. In some embodiments, additional dataand/or types of data are obtained from various publicly or privatelyavailable sources.

As shown, the data inputs 113, 115, 117 are able to enter the system 100through a publish-subscriber messaging service 144. In some embodiments,the messaging service 144 is a publisher stream service for low-latency,real-time data dissemination. In some embodiments, the messaging service144 is distributed across multiple nodes or servers using a distributedmanagement service, such as Zookeeper. In some embodiments, additionalfunctionality (e.g., efficient distribution across multiple nodes orservers) for the messaging service 144 is provided by commerciallyavailable software, such as Kafka. Using the messaging service 144, thesystem 100 specifies the data to be received from the input data 113,115, 117 and directs the data to a specific component for analysis. Insome embodiments, incoming data is published as message(s) on thesubscriber/publisher service, and various components optionallysubscribe to the service to pull the relevant data. In some embodiments,the messaging service 144 filters the data inputs 113, 115, 117 usingspecified spatial or attribute constraints.

In some embodiments, the system 100 includes an Extract Transform Load(ETL) layer 146 for pre-processing the data inputs 112, 122, 132. Asshown, the ETL layer extracts the data from the messaging service 144,transforms the data into proper format or structure for storage in theMaster Database 154 and analysis on the Prediction Models module 156 andthe Prediction Engine 166. The ETL layer 146 loads the properlyformatted data onto the Master Database 154 for batch analysis or passesit to the Prediction Engine 166 for real-time analysis. In someembodiments, in the batch layer 150, the ETL 146 loads the data ontoMaster Database 154 for storage on a periodic basis, such as on anhourly, daily, weekly, monthly basis. In some embodiments, in thereal-time layer 160, the ETL 146 loads the data as soon as it isprocessed with new input data or periodically after small timeincrements such as 30 seconds, 1 minute, 5 minutes, 15 minutes, etc. Insome embodiments, a small time increment is a time block.

In some embodiments, the ETL 146 provides data validation to confirmwhether the data from the input sources has expected values based on oneor more data validation rules. In some embodiments, the ETL 146partially or wholly rejects a data entry that fails the one or morevalidation rules. For example, in some embodiments, validation for afield for a U.S. phone number for an emergency call requires 10 digits.In some embodiments, a field with emergency type or nature is expectedto have words such as “medical”, “fire”, etc. for validation. In someembodiments, if incoming data for that field has digits instead ofcharacters, the corresponding emergency entry/entries are recorded as avalidation error in the error log and the digits are optionally replacedwith “unknown” or “none.” In some embodiments, the ETL 146 reports therejected data entry to the source for further analysis.

In some embodiments, the ETL 146 transforms the input data 112, 122, 132to meet the technical needs of the components downstream. In someembodiments, the ETL 146 applies a set of rules or functions on theinput data input data 112, 122, 132. In some embodiments, data is in theproper format and successfully passes through the ETL 146. In someembodiments, the ETL 146 transforms the data by transforming codedvalues or deriving a calculated value. In some embodiments, the ETL 146transforms data by joining, combining, or merging data from multiplesources. In some embodiments, the ETL 146 combines proprietary emergencydata and available emergency data. In some embodiments, the ETL 146removes duplicate entries in the data. In some embodiments, the ETL 146uses a commercially or publicly available processing engine, such asSpark.

In some embodiments, the Master Database 154 stores the pre-processedinput data 112, 122, 132. In some embodiments, the master database 154is a database or a collection of databases or a database managementsystem. In some embodiments, the master database 154 is a relationaldatabase (such as an SQL server, Oracle Database, Sybase, Informix andMySQL. In some embodiments, the master database 154 is hosted one ormore computers in a distributed system. In some embodiments, the masterdatabase 154 stores the data and provides access to the Predictive Modelmodule 156.

In the batch layer 150, in some embodiments, the Predictive Model module156 analyzes the data and generates a model or algorithm for makingemergency predictions. Various techniques, models or algorithms are usedin the Predictive Model module 156. In some embodiments, after modelgeneration or training of the algorithm, the Predictive Model module 156queries the model with input data (e.g., an emergency data set) forgenerating an emergency prediction. In some embodiments, emergencypredictions are saved in the batch serving database 158 and is madeaccessible using one or more output services 190. In some embodiments,output services 190 includes one or more of a query services 191,visualization/mapping 192, analytics 194, web applications 187, andmobile applications 189.

In some embodiments, the output services 190 are used by one or moreusers, customers and/or administrators of the system 100. In someembodiments, output services are accessible by users, administrators orcustomers of the emergency prediction system. In some embodiments, asoftware module in the EMS provides access to the emergency predictionsystem. In some embodiments, a software module in the PSAP systemprovides access to the dispatch operators or administrators to accessthe emergency prediction system.

In some embodiments, in the real-time layer 160, the Prediction Engine166 analyzes the input data to make one or more predictions in real-timeor small increments of time. Various techniques, models or algorithmsmay be used in the Predictive Engine 166. In some embodiments, aftermodel generation or training of the algorithm, the Predictive Engine 166queries the model with input data to generate an emergency prediction.In some embodiments, the emergency predictions are saved in thereal-time serving database 168 and made accessible using one or moreoutput services 190.

In some embodiments, the Predictive Model module 156 and the PredictiveEngine 166 use a processing engine such as Spark. In some embodiments,the models and algorithms are programmed using Python.

FIG. 2 illustrates an exemplary system component for an emergencyprediction system 200. As shown in FIG. 2, several input data 212, 222,232, 242 enter the system 200 and outputs are generated in the batchlayer 250 or a real-time layer 260. In some embodiments, the call datastream 212 is a real-time stream of data of incoming emergency calls. Insome embodiments, the call data steam 212 is a proprietary call datastream or a third party call data stream (e.g., a third-party emergencycall handler system or a data stream from a PSAP). In some embodiments,the data stream 212 includes a call identifier (e.g., phone number), atime stamp, and the location (e.g., a latitude or range, a longitude orrange, an elevation or range, a physical address, floor or suite number,etc.) of the device from which the emergency call is made.

In some embodiments, emergency data 222 is an input into the system 200.In some embodiments, emergency data 222 comprises proprietary emergencydata sent from subject devices. In some embodiments, emergency data 222is obtained from one or more databases in the EMS or other remoteservers. In some embodiments, proprietary emergency data 224 isgenerated when real-time call data from the data stream 212 are saved inthe Master Database 154 (shown in FIG. 1).

In some embodiments, additional data 232 forms an input into the system200. In some embodiments, additional data 232 is obtained from one ormore public or privately available sources. In some embodiments,additional data 232 comprises environmental data. In some embodiments,environmental data is comprises information on traffic condition,weather condition, road condition, or any combination thereof. In someembodiments, additional data 232 comprises information aboutdemographics, population density, etc., regarding the area, which isuseful for making emergency prediction(s). In some embodiments,additional data 232 includes information about geographical orjurisdictional boundaries for PSAPs, counties, towns, cities, or othergovernment borders. In some embodiments, additional data 232 includesinformation on a concert, sporting event, political demonstration,festival, performance, riot, protest, parade, convention, politicalcampaign event, or any combination thereof.

In some embodiments, emergency resource data 242 is an input into thesystem 200. In some embodiments, emergency resource data 242 is obtainedfrom one or more public or privately available sources. In someembodiments, emergency resource data 242 comprises the number and/ortypes of emergency resources and personnel available to respond to anemergency call within a jurisdiction, such as a PSAP area, county, city,etc. (also referred to as local emergency resources 241). In someembodiments, the local emergency resources 241 for a specificjurisdiction comprises emergency response vehicles (e.g., fire engines),emergency response personnel (e.g., fire fighters), emergency responseequipment (e.g., fire extinguishers), emergency response bases (e.g.,fire stations), or any combination thereof. In some embodiments,emergency resource allocation data comprises number or amount of localemergency resources, location of local emergency resources (e.g., x-ycoordinates, physical address), restraints on allocation of localemergency resources (e.g., availability of personnel during holidays,repair and maintenance of vehicles and equipment, etc.), restraints ondispatch of local emergency resources (e.g., down-time betweendispatches, inaccessible locations, etc.), or any combination thereof.

In some embodiments, emergency resource data 242 is obtained from apartner, such as a PSAP, county, city, state, region, or anothergovernment-defined area, for the purpose of optimizing emergencyresource allocation. For example, in some embodiments, a PSAP providesinformation about police resources including number of police and squadcars to obtain a recommendation for allocation of the emergencyresources for responding to emergencies within the PSAP jurisdiction. Insome embodiments, the emergency resource data 242 is obtained from atleast one publicly available source. For example, in some embodiments,the locations of police stations are available from the county websiteor mapping service.

In the batch layer 250, emergency data 222 is processed by addingenvironmental features by merging with environmental data from theadditional data 232, in various embodiments. For example, in someembodiments, weather and/or traffic features from the time and locationof each call in the emergency data 222 are added to create additionaldata augmented emergency data (e.g., augmented with additional data),which is a type of augmented emergency data. In some embodiments, otheradditional data such as geographical features are also added toemergency data to generate augmented emergency data. Other types offeatures, such as derived from event data may also be augmented. Anexemplary additional data augmented emergency data is depicted in Table2.

In some embodiments, after additional features are added, the additionaldata augmented emergency data is inputted into the spatiotemporalemergency call prediction module 220 for creating a prediction model forgenerating at least one spatiotemporal emergency prediction. In someembodiments, for generating one or more spatiotemporal emergencyprediction (e.g., predicted number of emergency calls, predictedemergency call density, kernel density estimates), the spatiotemporalemergency call prediction module 220 creates one or more predictionmodels. In some embodiments, the appropriate prediction model is chosenbased on the fit or prediction accuracy. In some embodiments, fitness isevaluated by calculating the error difference between the predicted andactual values. In some embodiments, the error difference is meanabsolute error, mean squared error, R², logarithmic loss, cross entropy,or hinge loss.

Various types of functions and algorithms are capable of being used tocreate the model, such as discrete location, continuous location (e.g.,Gaussian density function, kernel density estimation (KDE)), or discreteand continuous time models. An exemplary method is depicted in FIGS.5A-5C.

In some embodiments, the prediction model is a generalized predictionmodel that is used to make spatiotemporal emergency predictions for anytype of emergency. In some embodiments, the prediction model isspecialized to a type of emergency such as a medical prediction model, afire prediction model, or a police prediction model. For modelgeneration for specific types of emergencies, some or part of theemergency data 222 that is inputted into the module 220 has to belabelled with the type of emergency, e.g., labeled emergency data. Insome embodiments, specialized prediction models are used to generatemore accurate emergency predictions for specific emergency type(s)and/or emergency priority. In some embodiments, when labeled emergencydata is not available, a generalized prediction module is created usingunlabeled call data. Similarly, in some embodiments, specialized modelsare used to generate more accurate emergency predictions for specificgeographic areas such as for specific different cities, counties, timeof year, etc., may also be beneficial. In some embodiments, augmentedemergency data, such as additional data augmented emergency data is usedfor creating prediction models that take into account additional data232, such as environmental data.

After one or more prediction models are created, one or morespatiotemporal emergency predictions corresponding to a definedgeographic area and a defined time period are generated. In someembodiments, an administrator or a customer sends a query for anemergency prediction (e.g., by using query service 291) for a definedgeographic area (such as a geographic block) and a defined time period(such as a time block). In some embodiments, if used in the predictionmodel, the system 200 obtains additional data such as forecastenvironmental data for the defined geographic area and the defined timeperiod. In some embodiments, the emergency predictions are generated byapplying one or more of the defined geographic area, defined timeperiod, and additional data (if needed) into the prediction model in themodule 220.

In other embodiments, the emergency predictions are generated on aperiodic basis batch-wise. Thus, in some embodiments, the emergencypredictions are generated every hour, day, week, month, or other timeblock. In some embodiments, the emergency prediction is saved in thebatch-serving database 158 (see FIG. 1) and made accessible by outputservices 290.

In other embodiments, the emergency data 222 is inputted into the intothe spatiotemporal emergency call prediction module 220 without addingfeatures. In some embodiments, the emergency data 222 undergoespre-processing in the messaging service 144 and ETL 146 before inputtinginto the module 220. Without the additional features, in someembodiments, the emergency prediction takes less time and less computingresources, but also with lower accurate. In some embodiments, anon-augmented model is preferred to get a quick emergency prediction,especially in the real-time layer. For example, in some embodiments, theemergency anomaly detection module 240 utilizes a “quick and dirty”model to make an emergency prediction for detecting clusters (notshown).

In some embodiments, the output of the spatiotemporal call predictionmodule 220 in the form of emergency predictions is accessible byadministrators, customers or users of the prediction system 200including the EMS. In some embodiments, various output services 290 areused to access the emergency predictions including query services 291 orcall density projections 296. For example, a sample output showingkernel density clusters for emergency calls is shown in FIG. 5C.

In some embodiments, the prediction model in the spatiotemporal callprediction is re-created or re-trained in the update model module 228.Various techniques and algorithms are capable of being used to determinea need for updating the model or algorithm. For example, in someembodiments, one or more historical spatiotemporal emergency callprediction is compared to an actual number of emergency calls todetermine the prediction accuracy. In the real-time layer, there is aneed for information regarding the type and priority of emergency callsin real-time or near real-time in the data stream 212 for makingaccurate emergency predictions. Provided herein are systems and methodsfor generating predicted labels for the data stream 212 and augmentingor incorporating the data stream with the predicted labels. In someembodiments, the Data Augmentation module 210 matches unlabeledemergency data with labeled emergency data to create a matched emergencydata. In some embodiments, the matched emergency data with labels andadditional information from the proprietary data stream is inputted intothe spatiotemporal prediction module 220 for a more accurate emergencyprediction.

Furthermore, in some embodiments, the Data Augmentation module 210develops a model or algorithm based on labeled emergency data and/ormatched emergency data for predicting labels for the data stream 212 inreal-time. An exemplary table for a data stream with predicted labels isshown in Table 3. In some embodiments, the model or algorithm forpredicting labels is re-generated or re-trained using actual labels fortype, priority, response time, or any combination thereof. In someembodiments, an exemplary table with the data stream with predictedlabels is used for updating the model after actual labels are availableas shown in Table 4. In some embodiments, the data augmentation module210 calculates the predicted response time for each emergency call inthe data stream 212 using a response prediction model. In someembodiments, the estimated response time is made available to anadministrator, user, or customer of the system 200.

In some embodiments, Emergency Anomaly Detection 240 (also referred toas Emergency Event Detection) provides early warning or triggerednotifications by detecting emergency events by automatically detectinganomalies. In some embodiments, an algorithm for anomaly detection isrun on a real-time or near real-time basis to identify emergency eventsthat are capable of affecting a group of subjects. Some such emergencyevents include an earthquake, landslide, tsunami, volcanic activity,wildfire, large-scale fire, cyclone, tornado, hurricane, epidemic,extreme temperature, industrial accident, chemical spill, nuclearaccident, terrorist attack, or large-scale transport accident.

In some embodiments, the call data stream 212 or the call data streamwith predicted labels is used to in an anomaly detection algorithm fordetecting clusters of emergency calls within a defined geographic areaand a defined time period. By using emergency data labeled with the typeand priority of the emergency and estimated response time, an earlywarning is generated and provided to the administrator, customer,dispatcher and/or subjects regarding the emergency. For example, in someembodiments, an early warning and notification system warns interestedparties about the emergency event and also the type and/or priority ofthe emergency.

As an illustrative example, as a call comes in, clusters of calls aredetected within a radius of the location of the call (e.g., within 1 km,2 km, or 5 km, including increments therein) and within a near real-timeperiod (e.g., 1 min, 2 min, 5 min, 10 min, or 15 min, includingincrements therein). Examples of near-real time include no more than 1,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25,30, 35, 40, 45, 50, 55, or 60 minutes from the time of a call orcommunication. For example, in some embodiments, the analysis of acommunication in near real-time includes analysis of the communicationwithin the recited time range of no more than 1 to 60 minutes from thetime of sending, receiving, and/or detecting the communication. In someembodiments, a cluster is detected by comparing the recent and on-goingcalls with historical or predicted call volume or density and detectingabnormal cluster of large number of emergency calls. In someembodiments, the expected number of calls is calculated by sampling thepredicted call density in an area, or in the case of the space-timepermutation scan statistic model, the expected number of calls is theproportion of calls that occur at a grid point times the total number ofcalls that occur in the time slot (see FIG. 5A).

In some embodiments, using the anomaly detection algorithm, a cluster isdetected when the actual number of calls exceeds the expected number ofcalls by a certain threshold. In some embodiments, after anomalies aredetected, one or more of users, customers and/or administrators of thesystem 200 or subjects who are likely to be affected by the emergencyevent are sent notifications 293. In some embodiments, subjects who areaffected by the emergency event are identified based on proximity orlocation within a detected anomaly. In some embodiments, the location ofa subject is obtained from a communication device of the subject such asa smart phone. Detailed description regarding the anomaly detectionmodule 240 are found in reference to FIGS. 6A and 6B.

As shown in FIG. 2, in some embodiments, emergency resource data 242enters the Emergency Resource Allocation module 270 for optimizingemergency resource allocation for responding to emergency calls within adispatch jurisdiction. In some embodiments, the emergency resource data242 is a local emergency resource data (e.g., for a PSAP jurisdiction).In some embodiments, an emergency resource recommendation (e.g., arecommendation allocating ERVs to particular base locations to minimizeresponse times) is generated based on the emergency data 222 and one ormore spatiotemporal call prediction from the spatiotemporal callprediction module 220. In some embodiments, an estimated response timeprediction is generated using a prediction model. In some embodiments,the prediction model is generated by analyzing labeled emergency dataincluding response times. In some embodiments, using an allocationalgorithm, the allocation of the emergency resource is optimized byusing an allocation simulator. In some embodiments, current or forecastadditional data 232 (e.g., weather and traffic data) is considered forthe emergency allocation recommendation.

In some embodiments, short-term (e.g., 1 hour, 1 day, 1 week)recommendations for emergency allocation are generated for short-termrecommendations (e.g., placement of emergency personnel or emergencyvehicles). In some embodiments, long-term predictions (e.g., a month,half-year or a year) are used for long-term recommendations (e.g.,budgeting and planning for hiring emergency personnel and purchasingemergency equipment). Detailed description regarding the anomalydetection module 240 are found in reference to FIG. 7.

In some embodiments, the dynamic resource reallocation module 278 allowsfor reallocation of the emergency resources based on predicted near-termcall data (e.g., for the next few hours). In some embodiments, thedynamic reallocation service 298 allows a user, a customer or anadministrator to access a dynamic reallocation recommendation. In someembodiments, after an event is detected affecting a group of subscriberswithin the emergency jurisdiction, the dynamic reallocation module 278provides one or more recommendations to respond to a cluster or hotspotof emergency calls. In some embodiments, the dynamic resourcereallocation module 278 allows for reallocation of emergency resourcesafter at least one emergency resource recommendation has been provided aperiod of time. This allows for reallocation in case predicted near-termcall data shows a likely misallocation of resources despite previousbest efforts at long-term predictions.

In some embodiments, the emergency predictions from the batch layer 250are saved in the batch serving database 158 (see FIG. 1). In someembodiments, the emergency predictions from the real-time layer 260 aresaved in the real-time serving database 168 (see FIG. 1). In someembodiments, the emergency predictions are accessible through an API 280using one or more output services 290. In some embodiments, outputservices 190 comprise one or more of a query service 291, emergencyevent detection and notification 293, call prioritization and routing295, hourly or weekly call density projections 296, emergency resourceallocation and planning 297, and dynamic reallocation 298.

In some embodiments, a platform for simulation is provided forallocating emergency resources (such as police cars). For example, insome embodiments, an administrator of the emergency predictions systemor of the EMS or a customer logging in at the PSAP system is able toaccess output services such as the simulation platform for providingestimated response times for responding to predicted emergencies. Insome embodiments, the administrator or customer is able to adjust theallocation (such as location of police cars) to see the predicted effecton response times and/or other results.

FIG. 3A illustrates an exemplary method for augmenting emergency calldata and data stream in the system for data augmentation 300. Severalvariations for augmenting data is contemplated based on the input intothe prediction model for making emergency predictions. Augmentation maydone with actual labels (e.g., weather conditions at time and locationof an emergency call) or with predicted labels (e.g., type or nature ofcalls because the label is not available or premature). Actual labelsmay also be referred to as “ground truth”

In some cases, the emergency data does not include labels or has missinglabels for some entries and these labels may be needed for accurateemergency predictions. Labels may include emergency type/nature (e.g.,fire, medical, police, pet rescue, road-side assistance), sub-types(e.g., theft, burglary, narcotics, domestic dispute), emergency priorityor severity (e.g., high, ambulance dispatch, officer visit, follow-upcall, no action needed), actual or estimated response time (e.g.,ambulance reached emergency location within 10 minutes), extent ofdamage (e.g., fatalities, injuries, property damage), etc.

In many cases, it is too early to know the label for entries in the calldata stream and it may be necessary to predict the labels as the call iscoming in (For example, “the incoming call is predicted to be a medicalcall requiring an ambulance dispatch?”). In the case for emergency data(e.g., historical call data), it may be the label for the call is notavailable or missing.

In some cases, the emergency data and/or data stream are augmented withadditional data such as weather, traffic, population density, PSAPjurisdiction, etc. before feeding into the model. For example, the calldata stream may need to be augmented with current weather conditions formaking accurate emergency predictions.

As shown in FIG. 3A, several inputs 312, 322, 332 enter the dataaugmentation module 310 and an output is generated including emergencycall data with predicted type or nature, priority and estimated responsetime (referred to as “Emergency Data with predicted labels” 389). Insome embodiments, the call data stream 312 is a proprietary data stream.In some embodiments, the call data stream is a real-time or nearreal-time call stream from a dispatch center or PSAP.

In many cases, the call data stream 312 does not include determinationsregarding the type or nature, priority and/or estimated response times(referred to as “labels”). Accordingly, in some embodiments, theselabels are used to apply the right predictive model and get accurateemergency predictions. In some embodiments, the data augmentation module310 predicts labels to the call data stream 312, which is optionallyused in the anomaly detection module 240, spatiotemporal call prediction220, emergency resource allocation module 270, or any combinationthereof (see FIG. 2).

In some embodiments, for predicting labels, labeled emergency data fromvarious sources such as PSAPs, EDCs and public and private sources isincluded in the emergency data 322. In some embodiments, the emergencydata 322 includes unlabeled emergency data with or without additionalinformation (see Table 1).

In some embodiments, the data stream 312 comprises the emergency calltime and the emergency call location (e.g.,latitude/longitude/elevation/address). In some embodiments, the datastream includes a calling device identifier such as a subject's phonenumber, account number, name or log-in ID, universal ID (uid), or anycombination thereof. In some embodiments, one or more additional fieldsare available depending on the source of the data stream, such as thecall device and network, accuracy of location information, and callduration. In some embodiments, one or more of these fields are includedas features in the predictive models for improving prediction accuracy.Exemplary raw or unlabeled data stream is shown in Table 1. Althoughdecimal digits in the latitude, longitude, and/or elevation coordinatesin the tables below have been excluded, various location coordinatedecimal values are contemplated. In some embodiments, locationcoordinates have values down to the 0.1, 0.01, 0.001, 0.0001, or 0.00001decimal places.

TABLE 1 Exemplary Raw/Unlabeled Call Data Stream Med Med Uid Call TimeLat Lon Elev Gen Age Hi Wt allerg cond e1b9 May 3, 2017 0:00 33 −117 199M 61 68 175 None NA e1b9 May 3, 2017 1:03 33 −117 199 F 52 70 285 NoneDiabetes dc3b May 3, 2017 5:27 33 −117 199 F 28 70 210 None — 8f0b May3, 2017 13:39 34 −118 36 F 25 69 130 Poison — Ivy 6d94 May 3, 2017 16:4929 −95 28 M 32 72 290 None — 2087 May 3, 2017 22:33 26 −98 38 M — — —None HBP d35b May 3, 2017 23:11 42 −84 287 F 60 69 200 None — 027a May4, 2017 19:50 26 −81 6 M 29 72 207 None — 67c8 May 4, 2017 20:00 35 −82643 M — — — Bactrim — 8f5ef May 4, 2017 20:24 31 −83 101 M — — NAPennicillin Sev Arth

In some embodiments, additional data 332 (e.g., geographical boundaries,traffic, weather, population density, demographics, human mobility,etc.) is used for data augmentation. In some embodiments, the additionaldata 332 is used for extracting features such as environmental features(weather, traffic information), event features (sporting events, etc.),and other features for incorporation in emergency data with predictedlabels 389 (e.g., augmented data). In some embodiments, theincorporation of environmental and other features in the labeledemergency data improves the accuracy of predictions generated usingmodels based on the emergency data with predicted labels. In someembodiments, emergency data augmented with additional data (even withoutpredicted labels) may be used in some prediction models (e.g., input 235for spatiotemporal prediction in FIG. 2).

In some embodiments, the proprietary data stream 312 is augmented withadditional data, such as environmental data in the data augmentationmodule 310. Table 2 shows a table with call data stream that has beenaugmented with environmental features (e.g., environment data).

TABLE 2 Call Data Stream Augmented with Environmental Features TrafficWeather Call Data Stream Geo Density Temp Prec Hum Uid Call Time Lat Lonurban zone road [#/mi] [F.] [cm] [%] elb9 May 3, 2017 0:00 33 −117 T ResT 9.0 94 0.2 0.5 elb9 May 3, 2017 1:03 33 −117 T Com T 6.0 56 0.5 0.6dc3b May 3, 2017 5:27 33 −117 T Com F nil 66 0.3 0.2 8fOb May 3, 201713:39 34 −118 F Com F nil 65 0.1 0.3 6d94 May 3, 2017 16:49 29 −95 F ComF nil 69 0.0 0.4 2087 May 3, 2017 22:33 26 −98 T Res F nil 92 0.0 0.4d35b May 3, 2017 23:11 42 −84 T Res T 3.0 88 0.4 0.8 027a May 4, 201719:50 26 −81 T Com T 2.0 76 0.8 0.8 67c8 May 4, 2017 20:00 35 −82 F ResF nil 89 0.2 0.4 8f5e May 4, 2017 20:24 31 −83 T Com T 6.0 90 0.2 0.1

In some embodiments, external data sources are used to augment theincoming call data stream 312. In some embodiments, these features areadded to each incoming call through either an API call to an externalservice or a query to a static database in the EMS.

In some embodiments, as shown in Table 2, when a new call occurs, aquery is made to a static geographic information database to determineif the call is in an urban/rural area, a residential/commercial zone,and/or on a road. In some embodiments, API calls are then made toexternal data services to provide the traffic density (if call occurs ona road) and current weather conditions such as temperature,precipitation, and humidity. In some embodiments, humidity is providedas absolute humidity (g/m³), relative humidity (e.g., 30% relativehumidity), or specific humidity (grams of vapor per kg of air).

Referring to FIG. 3A, in some embodiments, matching is done by variousmethods in the matching module 316. For example, in some embodiments,the emergency data is first matched with existing proprietary emergencydata. In some embodiments, near-to-exact matching of call time andlocation is done. In some embodiments, a buffer threshold is necessaryto account for differences in reporting resolution from PSAP data. Insome embodiments, an error such as, for example, a root mean squared(RMS) error term is appended to the matched data to store a record ofthe match accuracy.

Next, predicted labels are generated for each call record in one or moreprediction models. In some embodiments, the predicted labels aregenerated by a multi-class classifier 334. In some embodiments, thematched emergency data from the matching module 316 (with or withoutadditional features) is used as the input to a multiclass classifier334. In some embodiments, the nature and priority of calls havedifferent criteria for each region or PSAP, and a model for eachdistinct region is generated. In some embodiments, several classifiermodels are assessed with the best performing model being selected usingmodel selection and validation 318. Sample output from the classifier334 is shown in Table 3.

TABLE 3 Call Data Stream with Predicted Labels Call Data StreamPredicted Features Uid Call Time Lat Lon p_nat p_fire p_police p_medp_highpri p_resp e1b9 May 3, 2017 0:00 33 −117 Fire 0.61 0.03 0.35 0.5715.0 e1b9 May 3, 2017 1:03 33 −117 Med 0.01 0.31 0.68 0.38 6.3 dc3b May3, 2017 5:27 33 −117 Fire 0.61 0.07 0.31 0.74 13.5 8f0b May 3, 201713:39 34 −118 Pol 0.19 0.55 0.27 0.11 5.4 6d94 May 3, 2017 16:49 29 −95Fire 0.64 0.03 0.34 0.17 3.3 2087 May 3, 2017 22:33 26 −98 Med 0.24 0.250.51 0.96 6.0 d35b May 3, 2017 23:11 42 −84 Pol 0.17 0.45 0.39 0.38 3.8027a May 4, 2017 19:50 26 −81 Fire 0.59 0.01 0.40 0.92 7.8 67c8 May 4,2017 20:00 35 −82 Med 0.45 0.06 0.49 0.09 2.0 8f5e May 4, 2017 20:24 31−83 Fire 0.55 0.02 0.44 0.84 9.7

As shown in Table 3, in some embodiments, the call nature probabilities(p_fire, p_police, p_med) are the output of the multiclass classifier.The class with the highest probability is assigned to p_nature. Here,p_highpri is the output of a classifier that determines the probabilitythat the call is a high priority level and p_resp is the output of aregressor or regression module 336 that provides the estimated responsetime in minutes. In some embodiments, the emergency type comprises acategory of emergency such as fire, medical, police, or fire. In someembodiments, the emergency type comprises one or more probabilities thata call or communication relates to one or more emergency types such asshown in Table 3.

In some embodiments, once the call data stream has been augmented andlabeled, it is stored in the master database 154 (see FIG. 1). At thispoint, in some embodiments, it is transformed into historical emergencycall data, or “proprietary emergency call data.”

In some embodiments, when emergency call data from other sources (e.g.,a PSAP) arrives, the data comprises metadata or information for eachcall that reflects the ground truth of the situation such as, forexample, the actual nature (e.g., emergency type) and/or priority of thecall. In some embodiments, the metadata or information comprises theactual response time, origin and identification of the response vehicle,final destination of the response vehicle, a description of theemergency, or any combination thereof. In some embodiments, the labeledemergency call data and the proprietary call data are matched using thecall time and location in the matching module 316. A sample of matchedemergency data is shown in Table 4, which is optionally utilized fortraining the predictive models applied to the incoming call data stream.

TABLE 4 Emergency Data Matched with Actual Labels Actual LabelsEmergency Call Data resp_time Uid Call Time Lat Lon . . . a_typepriority [mins] e1b9 May 3, 2017 0:00 33 −117 . . . Fire High 17.5 e1b9May 3, 2017 1:03 33 −117 . . . Med Med 4.5 dc3b May 3, 2017 5:27 33 −117. . . Fire Med 12 8f0b May 3, 2017 13:39 34 −118 . . . Pol Low 12 6d94May 3, 2017 16:49 29 −95 . . . Fire Med 8 2087 May 3, 2017 22:33 26 −98. . . Med High 6.2 d35b May 3, 2017 23:11 42 −84 . . . Med Med 11 027aMay 4, 2017 19:50 26 −81 . . . Fire High 6.7 67c8 May 4, 2017 20:00 35−82 . . . Fire Low 3 8f5e May 4, 2017 20:24 31 −83 . . . Fire High 11.6

Referring to FIG. 3A, after predicted labels are generated, thepredictions may be validated in the validation module 318. Variousmethods of validation are contemplated. For example, in someembodiments, the emergency data with both predicted and actual labelsare used to validate the prediction accuracy periodically or on-demand.

The validation process may be executed in batch either when emergencycall data is added to the database or at a set time interval. FIG. 8shows exemplary validation data. The shaded values indicate where thepredicted and actual features were inconsistent. For rows 7 and 9, thecall nature prediction may be incorrect. For row 3, a prediction has74.7% probability that it was a high priority call. However, the actualcall was not high priority. For rows 4, 5, and 7, the predicted responsetimes are smaller than the actual response time by a thresholdpercentage (greater than 50% difference). In some embodiments, whenvalidation is unsuccessful, retraining or recalculation of the model iscarried out. In some embodiments, a threshold for the minimum predictionaccuracy is set (e.g., a percent of predictions that meet the accuracycriteria). In some embodiments, the threshold is at least 50%, 60%, 70%,80%, 90%, 95%, 99%, or more, including increments therein. In someembodiments, when this threshold is not met, retraining of the models istriggered and/or a notification to the administrator of the system 300is sent indicating a failure to meet the minimum prediction accuracythreshold.

FIG. 3B illustrates an exemplary method for augmenting emergency dataand data stream in the system for data augmentation 380 for real-time orbatch emergency predictions. A call data stream 312 is received andsubject to data augmentation 382 using data from one or more staticdatabases such as, for example, geographic population density data,physical address, zoning and jurisdictional boundaries, etc. Becausestatic data does not change on a continuous basis, databases with suchinformation may be obtained internally with periodic updates.

For example, as each call comes in, it is processed and transformed innear real-time or real-time in the ETL (see 146 in FIG. 1). For eachcall entry, the location, time and identifying information about thecall (such as user phone number, device ID) may be extracted. Using thisinformation, the Data Stream Augmentation Module 314 may query databaseswithin the EPS for information to be matched to the call in the step382.

Next, in some embodiments, the data stream 312 is augmented with datafrom dynamic or external data sources such as, for example, weather,traffic, and other information in step 384. The dynamic data may changefrequently such as weather conditions, traffic density, etc. In step384, API calls may be made to relevant sources for dynamic data. It isunderstood that the steps 382 and 384 may be conducted simultaneously orin a different order. In some cases, external data sources may beconsulted to get static data in step 384 when such information has notbeen obtained internally. The returned static or dynamic data may bestored in corresponding columns and added to the call record (i.e., calldata stream or emergency data).

In some embodiments, one or more prediction models are used to predictlabel(s) for the call data stream such as, for example, nature of theemergency (e.g., emergency type), call priority (e.g., emergencypriority), and/or emergency response time in step 386 (via, e.g., thedata augmentation module 310 in FIG. 3A). The predicted labels may beadded to the call record in corresponding columns. In some embodiments,the predicted labels are incorporated into the call data stream.

The labeled emergency data may be used for real-time emergencyprediction (step 360) and/or stored in the Master DB (see 154 in FIG. 1)for use in batch emergency prediction (see 359). For example, theaugmented emergency data/data stream is optionally sent to a real-timelayer for emergency event detection (e.g., emergency anomaly detection)in step 360.

In some embodiments, emergency data is matched to proprietary emergencydata 316. The matching may be used to collect available informationabout a particular call, resolve discrepancies and remove duplicates. Insome embodiments, matching data is used to validate predictions. In someembodiments, the predictions are assessed for accuracy such as arequirement to exceed an accuracy threshold 328 (not shown). In someembodiments, a prediction model is re-trained if the accuracy thresholdis not met. In some embodiments, the matched (and sometimes unmatched)data is augmented with additional information such as, for example,geographic, population density, weather, traffic, or other data 330. Insome embodiments, the matched data is used for generating spatiotemporalprediction models for making batch predictions 350. In some embodiments,the data stream is proprietary data such as current or recent 911 calldata that does not have publicly available label information. In someembodiments, the proprietary data is augmented with predicted labels 388generated by one or more prediction models. In some embodiments, theaugmented proprietary data is matched with emergency data 316. In someembodiments, methods used for data augmentation include extrapolation,tagging, aggregation, probability, or any combination thereof.

In some embodiments, proprietary emergency data may be available and maybe used for batch emergency prediction as shown in FIG. 3B. In someembodiments, emergency data from other sources (e.g., a PSAP, a dispatchcenter, a call center) may be obtained by the EPS in a stream or byperiodic updates. When emergency data is obtained from differentsources, there may be variations in recordkeeping. For example, a PSAPmay save information about the response time, nature or type ofemergency, etc. in the emergency data (labeled emergency data), whileanother PSAP may not. If the proprietary emergency data includes asub-set of the calls (e.g., only the emergency calls made from mobilephones), then the only a sub-set of calls from the proprietary datastream will be matched to the emergency data from the PSAP (which willinclude both landline and mobile phone calls).

For emergency data 322, a match with proprietary data (or emergency datafrom another source) may be sought in step 316. If there is a match withproprietary data, environmental features (e.g., weather or trafficinformation) from environmental data (and/or other additional data) werealready augmented along with predicted labels in the data streamaugmentation 314. Matching call entries may be joined into single recordand may include the actual labels or ground truth (if available). For asub-set of call entries with actual and predicted labels, validation ofthe predictions may be done in step 318. If the threshold for theminimum prediction accuracy or “error threshold” between the predictedand actual labels is not met for a given number call records, the systemmay trigger a re-training or update of the prediction model (not shown).

If there are no matching call record found in step 316, environmentalfeatures may be added in step 330. In some embodiments, predicted labelsmay be added in a step 386 (not shown), if needed, followed by avalidation process (step 318).

After processing of emergency data (e.g., augmentation withenvironmental feature and/or predicted labels), the emergency data maybe saved in a database 387 (also referred to as the “processed emergencydata DB”) for future use for generating emergency predictions.Variations for processing of emergency data are contemplated.

Most cities utilize predictive models for estimating emergency responseservice demand, either in aggregate for resource planning or in finerspatiotemporal resolution for dynamic resource allocation anddeployment. These models are subject to poor approximations due tosparsity of emergency event data at higher spatiotemporal resolutions.Using models described in this disclosure has the potential tofacilitate better resource planning, decrease response times, anddecrease costs associated with over-allocation of emergency units orad-hoc reallocation of services to cover underestimated demand.

FIG. 4A depicts a schematic diagram for implementing one embodiment of amethod for generating spatiotemporal emergency prediction, specificallyfor calculation of estimated emergency call densities for a definedgeographic area and time period. In some embodiments, labeled emergencycall data 427 is sourced from one or more PSAPs serving one or moregeographic areas. In some embodiments, labeled emergency call datacomprises label information on emergency calls such as, for example,emergency type and/or emergency priority. In some embodiments, anemergency type is a medical emergency, a police emergency, or a fireemergency. In some embodiments, emergency priority is a high priority ora low priority. In some embodiments, emergency priority is defined bythe PSAP the labeled emergency call data is obtained from. In someembodiments, labeled emergency call data is used to train or generateemergency type-specific prediction models such as, for example, amedical call predictive model, a fire call predictive model, or a policecall predictive model. In some embodiments, proprietary emergency calldata 424, which is sometimes unlabeled emergency call data, is obtained.

In some embodiments, labeled emergency data 427 and proprietaryemergency call data 424 is subject to one or more call filters 438 tofocus on the region and time of interest. Regional filters may benecessary not only to constrain the amount of data being queried, butalso because there may be specific attributes of that region that mustbe applied to the model. For example, the city of Chicago may applydifferent criteria for what constitutes a “High Priority” medical callthan what Seattle uses. Emergency data may be also filtered using thetime of the call, based on the time window for the emergency prediction.For example, if our spatiotemporal call prediction model produces a calldensity prediction for a given hour of the week, and the model dependson data from that same hour for the past 8 weeks, we only want to querythat specific data to reduce computational expense of processing amassive amount of data. Each prediction model in module 420 may eitheruse the same architecture or be modified for the nature of call oremergency type and region, depending on experimental results.

In some embodiments, the call records may be filled using call nature oremergency type and/or emergency priority. As shown in FIG. 4A,prediction models for police, fire and medical emergencies may bedifferent (see 442, 444, 446). In some embodiments, by generatingprediction models for different types of emergencies, the emergencypredictions is more accurate and allows optimization of resourceallocation for specific type of emergency resource (e.g., medicalresources, police resources).

Although not shown, prediction models for specific priority levels arealso contemplated. For example, emergency call density estimates for“high priority” fire emergency calls for each hour for the next week maybe generated. The local fire department may use these predictions forresource planning for the upcoming week.

In some embodiments, a generalized model 448 may be used for generationof call density for all emergency types and priority. In someembodiments, unlabeled data is optionally used for generating the model.In some embodiments, the output comprises the predicted call density perhour in a region. For example, a PSAP that is unable to provide updated,labeled call data on a regular basis, but would like an estimate howmany call takers or dispatchers should be assigned for the upcomingweek. For resource planning, in some embodiments, the output of ageneralized model 448 is used to estimate the total number of calls perday.

Thus, in some embodiments, unfiltered data is used to generate or traina generalized model 448 not based on a specific emergency type. Here,the proprietary emergency call data 424 lacks label information such asemergency type, and as a result cannot be used for emergency specificpredictions without further data augmentation. In some embodiments,proprietary emergency call data 424 is sent to an emergency call recordpoint cloud 434, and subsequently used to help generate or trainemergency specific predictive models.

In some embodiments, the point cloud is a repository of call data fromthe geographical area or timeframe of interest (or the spatiotemporalspace that is being estimated). The point cloud may include allavailable call records including unlabeled emergency data as long as itincludes location data. Using the point cloud, a high-dimension space tobe approximated by a lower-dimension point cloud by capturing geographicand temporal characteristics. In some embodiments, to use the pointcloud, points in a cloud in the geographic area (and/or temporal space,emergency type, emergency priority, etc.) are sampled to form anadjacency graph, which are optionally incorporated in the spatiotemporalemergency prediction based on concepts such as manifold learning theory.For example, assuming that an emergency call will only be made from alocation where the caller is actually located, since they are dialing ontheir mobile device or a landline, it is expected that a high density ofemergency calls will be made from urban areas or on highwayscorresponding to a high population density. Conversely, low density orno emergency calls may be expected from the middle of a lake or a denseforest. Emergency calls may still occur in these areas (e.g., a boatingaccident or a lost hiker), but in observing the locations of all of thecalls in aggregate, we begin to see the “shape” of the city and have abetter understanding of where calls are likely to occur. Thisfacilitates more efficient computation of call predictions, since we donot waste time calculating predictions in areas where people are notlikely to be located. In this way, population density and human mobilitymay be captured in the spatiotemporal model.

In some embodiments, the underlying spatiotemporal characteristics maychange over time and a point cloud with the newest data (e.g., currentemergency data, proprietary data stream) may provide a currentrepresentation of the spatiotemporal geometry for the model. In someembodiments, the emergency data that is sampled in the point cloud maybe chosen to focus on the geographic area, time frame or emergency typeof interest, which may be a specific date of interest (e.g., daylightsavings day), time of day (e.g., afternoon), etc. In some embodiments,the one or more prediction models are generated by a spatiotemporalemergency call prediction module 420. In some embodiments, the module420 generates one or more batch predictions, which are optionally storedon a batch serving database 458. In some embodiments, the predictionresults are made available or provided to certain administrators, users,customers, emergency services, or others via one or more of dashboards,analytics, and resource management tools 490.

In some embodiments, labeled emergency data 427 and proprietaryemergency data 424 are input into the spatiotemporal module 420. In someembodiments, obtaining labeled call data requires data-sharingagreements with municipal and county data resources. Therefore, in someembodiments, this method is capable of producing category-agnosticpredictions of call volume converting proprietary unlabeled emergencydata into the labeled emergency data. This provides a readily accessibletool for emergency resource planning (e.g., by a PSAP), and serves as abaseline model for future enhancements as additional sources becomeavailable. In some cases, partially labeled or unlabeled emergency datafor spatiotemporal emergency prediction.

In some embodiments, a model undergoes retraining or recalculating amodel. In some embodiments, retraining or recalculating takes place atleast every hour, day, week, month, or other time block. In someembodiments, data regarding geographical boundaries are used forproviding visualization of one or more predictions to a subject such as,for example, an EDC, an EMS, a PSAP, an administrator, customer(s),user(s) affected by an emergency prediction, or emergency responsepersonnel. In some embodiments, data regarding geographical boundariesis used for visualization (e.g., PSAP, zip code, census tract, city,etc.).

Incorporation of proprietary emergency data into the module 420 hasseveral advantages. For example, including proprietary data streamentries in the point cloud 434 enables the capturing of current trends.In some areas, the underlying spatial characteristics change over timeand by incorporating the recent emergency data in the point cloud, amore current representation of the spatial geometry is incorporated inthe model. In some embodiments, recent emergency data is included in thepoint cloud to generate an up-to-date model even when the labeledemergency data may be older. In addition, in some embodiments, theproprietary emergency data comprises additional information about thesubject including demographic information (e.g., age, sex, height,weight, etc.), medical information (e.g., allergies, conditions, etc.),other information (e.g., call duration, device type, etc.). In someembodiments, additional data, such as environmental data is incorporatedinto the module 420 to improve accuracy.

FIG. 4B depicts a timing diagram 452 for implementing one embodiment ofa method for generating spatiotemporal emergency prediction. In someembodiments, a weekly spatiotemporal model is used to capture the weeklyseasonality in emergency data and make spatiotemporal predictionsregarding an upcoming week. As shown, the hours of a week (168 hours)are depicted in this embodiment.

In some embodiments, emergency predictions for volume or density foremergency calls for a defined geographic area (e.g., an entire city,county, or PSAP) and defined time period (e.g., 1 hour) is used forimproving emergency response and planning. For this purpose, aspatiotemporal model is capable of providing near-term predictions atdesired resolutions (e.g., 1 hour, 12 hours, 1 day, 1 week, 1 month, orother defined time period or time block.).

In some embodiments, for making emergency predictions for the future(e.g., a week later), emergency call data from the previous week andback to at least the previous M weeks is needed. In some embodiments,emergency data is based on a desired sliding time window M, whichincludes location, time, and optionally, additional information (such ascall duration, response time, and/or assigned PSAP). In someembodiments, emergency data from multiple years are used for comparingpredictions with aggregate averaging methods. In some embodiments, thismodel uses call data labeled by response category and/or priority.

In some embodiments, the spatiotemporal predictions are made on asmaller geographical area (1 km×1 km area) and aggregated over a largerarea (e.g., a PSAP, zip, tract). Thus, spatiotemporal predictions usinga point cloud (based on an adjacency graph) need not be constrained to agridded area. Similarly, in some embodiments, the spatiotemporalpredictions are made for one or more time blocks per day and aggregated.In some embodiments, aggregation is for 4, 8, 12, or 24 hours, includingincrements therein.

In some embodiments, specialized models improve the prediction accuracyof the spatiotemporal emergency call prediction. For example, in someembodiments, emergency calls requiring an ambulance dispatch(corresponding to high-level of priority) are modeled separately fromnon-ambulance emergency calls. In some embodiments, specialized modelsfor different types of emergencies (medical, fire, police), for largetimeframes (e.g., 1 month, one season, 1 year, etc.), for large areas(e.g., towns, cities, regions) are created and used to generatepredictions. In some embodiments, incorporation of humanmobility/population density is used to improve the prediction accuracy.In some embodiments, prediction(s) based on type of emergencies (e.g.,medical emergencies) are helpful in planning and allocating emergencymedical resources (e.g., ambulance, EMTs, etc.).

In some embodiments, the spatiotemporal predictive models are basedprimarily on recent and historical emergency call data (e.g., 911 calldata). Various models may be used for generating the spatiotemporalpredictions including discrete location, continuous location (e.g.,Gaussian density function, kernel density estimation (KDE)), discreteand continuous time models. FIG. 5A depicts a method for generatingspatiotemporal emergency prediction using kernel warping technique. Asshown, in certain embodiments, a Kernel WARP model is used forgenerating spatiotemporal predictions. A benefit of the kernel warpingmethod is that it utilizes a graph Laplacian of a larger point cloud ofdata, which can be interpreted in a Bayesian sense as imposing a priorthat accounts for spatial features. Thus, the historical emergency calldata is leveraged as a sort of “map” of an area and reduces the need forincluding complex boundary conditions that can be computationallyexpensive compared to other models such as generalized methods ofmoments (GMM), Smoothing Technique Kernel Density Estimate (stKDE).

In some embodiments, emergency predictions are generated and stored foreach available geographic region and emergency category: Fire, Medical,and Police. Other categories (water rescue, pet rescue, car crash, etc.)and sub-categories (e.g., crime categories, etc.) are contemplated. Insome embodiments, emergencies may be categorized into more than onetype. For example, in some embodiments, a car crash requires assistancefrom fire, medical and police emergency resources.

In some embodiments, a variety of priority levels are used to labelemergency calls. For example, in some embodiments, an emergency thatrequires dispatching an ambulance is categorized by “Priority,” “HighPriority,” “High Severity,” “High Risk,” or other priority level. Insome embodiments, two to five levels of priority are used by an EDC orPSAP. In some embodiments, emergency data obtained from that EDC or PSAPis filtered for each priority level. In some embodiments, one or morespecialized models for specific priority levels are generated.

As shown, labeled emergency data 527 may be generating thespatiotemporal emergency prediction using Kernel Warping. The labeleddata 527 may be filtered by region [r], category [c] or emergency type,priority [p], hour of the week [t], number of weeks [M], etc. to focuson the region and time of interest.

The point cloud 534 is a large repository of points (such as emergencycalls from a specific area) that represents the spatial and geographiccharacteristics of the area that the labeled data covers. In someembodiments, it is assumed that information about the high-dimensionspace is capable of being approximated by a lower-dimension point cloud.To use the point cloud, points in the point cloud 534 are sampled toform an adjacency graph representing the spatial geometry of the area.In this way, the Kernel warping takes into account “the spatialcharacteristics of the area for generating emergency predictions.

In an exemplary embodiment, the 1-hour spatiotemporal kernel densityestimate (stKDE) may be calculated (step 541). Then, the labeled datamay be clustered into sub-region components (step 543). From the pointcloud 534, “n” spatial points around each component may be sampled (step545) and an adjacency graph and Laplacian Matrix may be constructed(step 547). Kernel warping may be done using equation [2] in step 549.

In some embodiments, a spatiotemporal prediction model is re-created orre-trained to improve prediction accuracy. Here, in certain embodiments,the spatiotemporal prediction model is updated using the followingsteps: (1) parameter calculations for H and λ at least once everydefined time period (e.g., once a week); and (2) predictions generatedon a sliding window for every time block in the next defined time period(e.g., every hour for the next week).

FIG. 5B depicts an exemplary input emergency call data on a map of CityX for visualization. City X may include several PSAP jurisdictions—PSAP1, PSAP 2, PSAP 3, etc. City X may also include several highways—Highway1, Highway 2, Highway 3 and geographical features such as Island 1. Asshown on map 552, emergency calls in exemplary emergency data arevisualized as dots (see, e.g., 553) on the map, in some embodiments.Using kernel warping, in some embodiments, the emergency data in FIG. 5Bis used to generate a kernel density map, as shown in FIG. 5C.

Referring to FIG. 5C, an exemplary kernel density map 558 is shown. Insome embodiments, the output from kernel warping is a set of predictedkernel density estimates (KDE) for calculated components in each regionwithin a time window/time block/defined time period (e.g., 1 hr.). Insome embodiments, the output includes date, hour, region, category,component, KDE, or any combination thereof. In some embodiments,predicted call volume in a given region or PSAP is extracted from theseestimated by computing the aggregate KDE sum over the area of interest.In some embodiments, in order to maintain fine resolution ofpredictions, the KDEs for each component are stored. In someembodiments, higher level aggregations or transformations are generatedas needed for analysis, reporting, visualization, or any combinationthereof.

As shown in FIG. 5C, in certain embodiments, KDEs (e.g., 554, 556) ofdifferent sizes are depicted as dark areas over the map of a city. Insome embodiments, geographical boundaries (e.g., county lines) oremergency jurisdictional boundaries (e.g., PSAP boundaries) are depictedin solid lines (e.g., 555). The KDE 554 is shown over the downtown areaof City X indicating a high density of calls. In some embodiments, thePSAP responsible for the area including KDE 554 (PSAP 1) may respond tothese emergencies by allocating emergency resources in the downtownarea. On the other hand, in some embodiments, the KDE 556 is located ina less densely populated area, but there may be a large number of callsdue to large volume of traffic on Highway 1. In some embodiments, thePSAP responsible for the area including KDE 556 (PSAP 3) may respond tothese emergencies by allocating emergency resources along the highway.In either case, the KDEs (554, 556) provide valuable emergencyinformation that is usable by a PSAP for emergency resource planning.

In some embodiments, using population density information, a predictionalgorithm normalizes for population and identify areas with high-levelof risk per capita. In some embodiments, additional data such asenvironmental data (such as weather, traffic, etc.), human mobilitydata, and/or event data are used to generate the prediction model forhigher prediction accuracy.

When a medium or large-scale emergency event occurs, it is common formultiple emergency calls to occur reporting the same incident. Forinstance, in the case of a traffic accident, occupants in several othervehicles in the area may call 911 within a brief time window. Theseemergency calls will likely come from a constrained geographic areawithin visible range of the accident. For a larger event such as anearthquake or a plane crash, many callers may report the event from awider geographic area given the high visibility of the incident. BecausePSAPs have limited and sometimes overlapping areas of responsibility,emergency service providers may not be aware of these related calls, andmay not understand that a larger-scale event is taking place thatrequires more emergency services than a typical call.

Provided herein are systems and methods for monitoring real-time or nearreal-time emergency calls and autonomously detecting clusters. In someembodiments, a cluster of emergency calls denotes a group emergency suchas a man-made or natural disaster. In some embodiments, disclosed hereinare early warning systems for notifying emergency resource centers andproviding subjects or users with notifications regarding significantemergency events. In some embodiments, cluster detecting approaches,such as expectation-based space-time permutation scan statistic,Bayesian scan statistic, and time series models for detectingacceleration of call volumes are used.

In some embodiments, the primary output of an emergency anomalydetection module is the detected clusters of emergency calls. In someembodiments, clusters are updated either at the time of each incomingcall or on a discrete schedule such as a time block or other time period(e.g., every 5 minutes, 15 minutes, etc.) depending on computationaldemand. In some embodiments, the output includes the center of thecluster, radius, start/end time, p-value, number of calls, expectednumber of calls, or any combination thereof.

In some embodiments, the clustering algorithm is run at a finerresolution than call prediction to enable near real-time notifications,such as every 5, 10, 15, 30, or 60 minutes, including incrementstherein. In some embodiments, one or more thresholds are used fordefining an “emergency event.”

FIG. 6A a method 600 for an emergency event or anomaly detectionutilizing a space-time permutation scan statistic. The process forcalculating the space-time permutation scan statistic are depicted insteps 642, 644, 646, 648.

As shown in FIG. 6A, one method for anomalous cluster detection is bycalculating a Poisson generalized likelihood ratio (GLR) and finding amaximum GLR. Call stream data 612 is obtained from one or more PSAPs andfiltered using call filters 638. For each call with location at point p,an anomaly detection module 640 applies a predictive model to the callstream data to detect any anomalies/clusters. In this exemplaryembodiment, a space time permutation scan statistic is used as shown inFIG. 6A to generate anomaly prediction(s) which is provided to areal-time serving database 660. In some embodiments, anomalypredictions/detections are provided to a subject or user via adashboard, analytics, resource management tools, or any combinationthereof 690.

As shown, the space-time permutation scan statistic first calculates thetotal number of calls Cover the time t in the area g, which is a circlewith radius r centered around the location of the call. It thencalculates the expected number of calls within that radius, and usesthat to calculate the Poisson generalized likelihood ratio (GLR). Thisprocess is repeated multiple times, gradually increasing the radius of rfrom r_min to r_max, and the result with the highest GLR is chosen. TheMonte Carlo method is then used to shuffle all of the points andre-calculate the GLR, which produces a p-value, or the probability thatthe point is within a cluster and not simply a random occurrence. If thepoint is indeed considered to be part of a cluster (and falls within apreset threshold defining a significant cluster), the result is storedin the real-time serving database 668 and used by output services 690(e.g., Dashboard, Analytics, Resource Management Tools)

FIG. 6B depicts time and spatial aspects of the anomalous clusterdetection. In some embodiments, time and area blocks are adjusted basedon the population density and call volume in the area. In someembodiments, the time block is 30 seconds, 1 minutes, 5 minutes, and 10minutes. 15 minutes, 30 minutes, etc. In some embodiments, the maximumradius is 0.5 km, 1 km, 3 km, 5 km, 8 km, 10 km, 15 km, 20 km, etc. Forexample, timing diagram 662 shows a time block “t” for anomalous clusterdetection, which may be 15 minutes. The spatial diagram 664 shows anarea block including the center of the space (r_(min)) and the outerradius (r_(max)). In some embodiments, the outer radius is at leastabout 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 km. In some embodiments, theouter radius is about 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 km.

Emergency response time is a critical factor in the outcome of manyemergencies. Response time is highly dependent on availability andproximity of number of emergency response personnel, vehicles andequipment including: number and location of emergency response vehicles(ERV) available at the time of the emergency, distance from calllocation, traffic conditions, nature and priority of call, and others.In order to minimize response time, dispatchers in PSAPs need tounderstand how these factors interact and optimize their resourcesaccordingly. Thus, emergency data or data stream with predicted responsetimes may be used for resource allocation and notifications.

Disclosed herein are systems and methods for optimizing and recommendingemergency resource allocation. As shown in FIG. 7, the system 700 hasinputs regarding local emergency resources 742, spatiotemporal emergencypredictions 771, emergency data with response times 774 and optionally,additional data 732 (e.g., weather). In some embodiments, a samplerequest is generated using spatiotemporal call density prediction(s).The availability data 742 (e.g., base locations, number of ERVs,allocation and dispatch constraints) may be inputted into the EPS by aresource manager or obtained from privately or publicly availablesources or statistics

In some embodiments, the sample request is analyzed by a regressionmodel to predict emergency response time(s) for the sample request(steps 782 & 784). In some embodiments, the regression model is trainedusing emergency data comprising response times 774 and optionallyadditional data 732. The predicted call response time(s) and samplerequests as well as local emergency resource availability data 742 maythen be used by the allocation simulator in step 776.

In some embodiments, the allocation simulator 776 runs one or moresimulations using the greedy allocation algorithm to optimize resourceallocation in step 775. In some embodiments, multiple simulations arerun to determine an optimized resource allocation. In some embodiments,the optimal resource allocation is generated ahead of time for morelong-term future allocation (e.g., generally at least a day in thefuture). Such information is helpful for staffing personnel, forinstance. In some embodiments, the optimal resource allocation isgenerated at least a day or week ahead of time (e.g., more long-termresource allocation on a daily or weekly basis) 779. Alternatively or incombination, the optimal resource allocation is performed as part of adynamic reallocation in which the model generates real-time, nearreal-time, and/or near term optimized resource allocation 778 (e.g.,near- or short-term such as within the next 1/4/8 hours). In someembodiments, both short-term and long-term allocation estimates(optimized resource allocation) are calculated. In some embodiments,allocation estimates are stored on a batch serving database 758 and usedby output services 760 (e.g., simulator, analytics) In some embodiments,the allocation estimates are provided to a user or administrator via adashboard, analytics, resource management tools, or any combinationthereof.

In some embodiments, additional data 732 for enhancing the modelincludes information about local emergency service resources orprojections, travel times based on traffic, significant event data. Foraccurate predictions of response times, in some embodiments, the modelincorporates complex allocation restraints such as required time betweentrips, reallocation travel time, non-availability conditions, etc.

The prediction accuracy of module 770 is tied to accurate predictions ofcall volumes by each hour of a day from the spatiotemporal module (notshown). In some embodiments, when the prediction accuracy forspatiotemporal emergency predictions is low in some PSAP areas, theallocation model is applied on historical emergency data, using a sampleof calls from the same hour of the day on multiple weeks as a proxy forpredicted demand.

In addition to the spatiotemporal emergency predictions, the module 770utilizes predictions from techniques for anomalous cluster detection,prediction of response times using data augmentation and other methods.

In some embodiments, projected short-term emergency resource levels fora geographic area are predicted using an objective function based onhotspot analysis. In some embodiments, by utilizing information onlocal-level emergency resources and providing an optimizedrecommendation for positioning and resource allocation, the module 770is able to make recommendations that are implemented by EDCs, PSAPs,private dispatch centers, mapping software, operations center, corporatesecurity, health systems, etc.

In some embodiments, the allocation model is specialized for specifictype of emergencies such as medical, fire, or police in order to focuson allocation of specific type of resources. In other embodiments,multiple emergency types are handled by the same model because manyemergencies need emergency response from various types of emergencyresources. For example, in some embodiments, an ambulance and a fireengine are both dispatched when there is a fire in a building.

Various techniques and methods are capable of being used for evaluationof simulations. An exemplary method is the greedy allocation algorithm,for example, for providing a recommendation for allocating ERVs to knownbase locations to respond to upcoming call volumes. Here, the objectiveis to allocate vehicles in such a way that response time is minimized.In some embodiments, risk based allocation is considered, whichoptimizes allocation to ensure a threshold of calls meet a targetresponse time (e.g., less than 10% of calls have a response time greaterthan 10 minutes).

In some embodiments, the availability and frequency of data on emergencyresources limits the resolution of this model to shift (8-hours) ordaily. In some embodiments, specialized models for higher resolution aredeveloped. In some embodiments, various models are capable of being usedfor anomalous cluster detection including unsupervised anomalydetection, supervised anomaly detection, or semi-supervised anomalydetection.

In some embodiments, in addition to daily and weekly allocation 779optimization and recommendations, dynamic reallocation of resources 778is recommended based on predicted near-term call volumes (in the next2-4 hours). For example, the current allocation of vehicles isconsidered and the impact on response time for reallocating them to meetupcoming demand. For example, in some embodiments, vehicles areidentified for moving to optimally meet the projected demand in 2 hours.

In some embodiments, anomalous cluster detection is carried out for acity, such as City X in FIG. 5B. In some embodiments, there are severalPSAP jurisdictions within City X, which handle emergency calls fromdifferent areas of the city. In some embodiments, when a collectiveemergency such as an earthquake or a terrorist attack occurs, there is asudden increase in emergency calls from certain sections of each PSAP.Using anomalous cluster detection, the clusters within different PSAPsare monitored in real-time or near real-time. In some embodiments,clusters of calls are detected in different PSAP areas. In someembodiments, notifications and/or recommendations regarding divertingemergency resources to affected locations are sent to the associatedPSAPs. In some embodiments, dynamic reallocation of emergency resourcesis carried out within different PSAP areas to respond to the groupemergency event.

Model Update

In some embodiments, the predictive models are updated to maintaindesired predicting accuracy level and producing acceptable results. Insome embodiments, when models are updated, new data that has becomeavailable since the last model update is added to the training or testset for the algorithm and/or the model parameters are recalculated withthe new data. In some embodiments, a parameter tuning process such as agrid search is used to select the best parameters.

In some embodiments, predictive models are evaluated for updating orautomatically updated at predetermined intervals or upon triggeredevents. In some embodiments, a predetermined interval is a time periodor time block such as, for example, 5 minutes, 15 minutes, 1 hour, 1week, or 1 month.

In some embodiments, updating the predictive models is triggered bycertain events that occur within the system. In some embodiments, atrigger is when new data becomes available. For example, when a certainPSAP has agreed to provide emergency call data, it triggers an updateand/or new prediction and gets recommendations for emergency resourceallocation. In some embodiments, a data ingestion tool monitors aconnection to the PSAP data source and imports the new data and triggersthe predictive model to update.

In some embodiments, model updates are modified by adjusting aprediction accuracy threshold. As new data arrives in the system, insome embodiments, a process compares the results of previous predictionswith the actual values. In some embodiments, when the difference islarger than a specific threshold, the model is updated. For example, amodel has made predictions of emergency call density within a PSAPregion for every hour of the day for a week. At the end of the week, thePSAP shares its emergency call records to be combined with theproprietary emergency call records, and a process compares the actualcall density that has occurred that week to the predictions using aselected fitness measure. If the fit is not within a determined range,the model is re-created (e.g., re-calculated or retrained) using themost current data.

In some embodiments, model updating is also dependent on thecomputational power required to train the model such as a predictionalgorithm. In some embodiments, more complex models require more time totrain and are trained infrequently to minimize down-time fromre-training/updating. Thus, in some embodiments, the triggers andthreshold for prediction accuracy are adjusted to reduce model updates.In some embodiments, less computationally expensive models are retrainedmore frequently, such as every minute, every 5 minutes, or some othertime block or time period, or whenever a new call comes in.

Updating Data Augmentation

In some embodiments, the data augmentation module (e.g., 210) providesone or more models that provide predictions to each call that arrives inthe call data stream. In some embodiments, a model is applied to eachincoming call in near real-time.

In some embodiments, the classification and regression models in thismodule are trained on labeled data in the proprietary database (e.g.,Master DB) or from other sources that have the actual labels includingresponse times matched with emergency calls. In some embodiments, thereare separate models trained for different geographic regions, based onfactors such as the availability of labeled data in each region anddifferences in local emergency response operating procedures that couldaffect model parameters. These separate models have the samearchitecture, but utilize different subsets of data to calculate andtrain the parameters.

Updates to modules and/or algorithms are dependent on the availabilityof labeled data. In some embodiments, models are triggered to updatewhen new data for the corresponding geographic region becomes available.In some embodiments, when there is a continuous stream of incominglabeled data, the updates are scheduled on a periodic basis or triggeredonce a certain number of new labeled records have arrived. In someembodiments, this module also utilizes new labeled data to validate theaccuracy of the model. In some embodiments, accuracy is calculated usinga fitness measure applied to the predictions made since the last modelupdate compared to the new labeled data. In some embodiments, when anaccuracy threshold is not met, the model is updated.

Updating Anomalous Cluster Detection

In some embodiments, the anomalous cluster detection module (e.g., 240)detects possible emergency events in near real-time by detectingclusters of calls. In some embodiments, the model contains severalparameters that is specific to a region, such as the time window andmaximum radius to scan for possible clusters. In some embodiments, toset the parameters, labeled emergency data is applied to the model andparameters are tuned to ensure a desired accuracy threshold is met. Insome embodiments, the model is then applied to incoming calls. In someembodiments, the model is validated by comparing the detected clustersfor incoming calls to actual emergency events once labeled data becomesavailable. In some embodiments, the prediction model is updatedperiodically or when triggered. For quick calculations, simplerpredictive models are used and updated more frequently.

Updating Spatiotemporal Call Prediction

In some embodiments, the spatiotemporal module (e.g., 220) provides calldensity predictions for a specific time period, such as every hour ofthe next week. In some embodiments, the predictions are recalculated ona frequent basis if desired (daily or hourly). In some embodiments, whentraining the model parameters is computationally expensive, updates aremade less frequently (e.g., no more than once every week, month, etc.).In some embodiments, in the case of the kernel warping method, the modelparameters related to the spatial characteristics of the region arerobust and are retrained once a week or less. Here, periodic modelupdate may be appropriate.

In some embodiments, updating the model and producing new predictions issomewhat dependent on the availability of labeled data. In someembodiments, when a PSAP provides labeled call data only once a week,updates are made for specialized prediction models based on labels suchas, for example, police, fire, or medical models on a weekly interval.Alternatively, in some embodiments, when there is an acceptable level ofprediction accuracy in the call nature and priority predictions in thedata augmentation model, the model leverages the proprietary emergencycall data to run more frequently and provide updated call densitypredictions.

In some embodiments, the model performance is validated by comparingpredictions to the actual call density when labeled data is available.In some embodiments, if the accuracy of the predictions falls below acertain threshold, the model is triggered to update by recalculatingparameters.

Updating Emergency Resource Allocation

In some embodiments, the emergency resource allocation module 270leverages the output of the spatiotemporal emergency prediction, andtherefore follows similar considerations for updating. In someembodiments, the parameters of this model are specific to each region(e.g., geographic area), since each will have different emergencyresource data and constraints. In some embodiments, the model isvalidated when labeled data becomes available and triggered for updateif certain thresholds are not met.

In some embodiments, a model is run weekly to provide resourceallocation recommendations for planning purposes. Alternatively, in someembodiments, if current information regarding the available emergencyresources in an area is available, the model is used to provide nearterm reallocation recommendations.

Emergency Auto-Detection by a Device

In some aspects, disclosed herein are methods for a communication deviceto determine, based on available data, if a user is in need of, or islikely to be in need of, emergency assistance, the method comprising:determining by the communication device, on an autonomous basis, andbased on available data regarding the user, data about the environmentaround the user, and information about the communication device, whetheror not the user of the communication device is in an emergency situationthat requires emergency assistance for the user; responsive todetermining, at the communication device, that the user of thecommunication device is in need of emergency assistance, and that theneed for emergency assistance for the emergency situation has not beenresponded to, constructing a request for emergency assistance based on acurrent status of the user derived from meta-data available about theuser, the communication device, and the environment around the user andthe communication device; sending the request for emergency assistanceto an EDC and/or EMS; responsive to detecting that the EMS and/or EDChas acknowledged reception of the request for emergency assistance,providing additional information to the EMS and/or EDC regarding datathat a decision to generate the emergency alert was based on; upondetermining that there is additional information available about theemergency situation in addition to the information already shared withthe user, the EDC, and/or the EMS, analyzing the additional informationalong with the information already shared, and upon determining that theanalysis yields a different conclusion about the user health status ascompared to user health status previously shared with the user, the EDC,and/or the EMS, sharing the different conclusion and the additionalinformation, with the user, the EMS, and/or the EDC; checking foradditional meta-data information about the user, the communicationdevice, and the environment on a periodic basis and, upon findingsignificantly new meta-data information, analyzing the additionalmeta-data information and sharing a result of the analysis of theadditional meta-data information and the additional meta-datainformation with the user, the EMS, and/or the EDC; and activelymanaging communication of the meta-data information and results ofanalysis performed on the meta-data between the communication device andany communication devices of the EMS and/or EDC. In some embodiments, ifan emergency situation is detected by the communication device, themethod further comprises determining, at the communication device, apotential threat to the user, a type of the threat, a possible impact ofthe threat to the user, the possible ways for resolving the threat,and/or potential sources of help available near to the user. In someembodiments, the method further comprises determining, at thecommunication device, whether to raise an alarm with the user of thecommunication device at an interface provided on the communicationdevice, in a situation in which the user is not under imminent threat.In some embodiments, the method further comprises determining, at thecommunication device, whether to raise the alarm includes determiningwhether to raise an alarm responsive to a raise in temperature of theenvironment approaching, but not yet having reached, a burningtemperature. In some embodiments, the request for emergency assistanceis sent to the EDC and/or EMS, responsive to the temperature of theenvironment having reached, or reaching, burning levels. In someembodiments, providing the additional information to the EMS and/or EDCincludes one or more of providing raw data that the decision to generatethe emergency alert was based on, providing a summary of the dataregarding the user, the environment around the user, and about thecommunication device, and presenting a conclusion relating to the healthstatus of the user and the reason for generating the request foremergency assistance to the EMS and/or EDC. In some embodiments, therequest for emergency assistance constructed and sent by thecommunication device includes meta-data containing information about thelocation of the user and the user and the communication device, healthdata about the user, and information about the environment. In furtherembodiments, the location information includes one or more of GPSlocation, history of GPS locations, cellular base station triangulationinformation from the most recent base station the communication devicewas associated with, Wi-Fi positioning information, and other form oflocation information. In further embodiments, the heath data includesone or more of health status of the user, user health history, andinformation sensed about the user by sensors at the communication deviceand available at the communication device. In yet further embodiments,the information sensed about the user by sensors at the communicationdevice includes information regarding one or more of user heart-beat,heart-rate, blood oxygen level, and pulse-rate. In further embodiments,the information about the environment includes one or more of airpressure, oxygen content in the air, carbon dioxide levels, and levelsof other gases of interest. In further embodiments, the meta-datafurther includes one or more of a phone number of the communicationdevice, a type of the communication device, and other relevantinformation about the communication device. In some embodiments, thecommunication device further makes a prediction, based on availablemeta-data about the user, communication device, and the environment,whether or not the user of the communication device is likely to be inone or more emergency situations, possible types of the one or moreemergency situations, a time before which the one or more emergencysituations will likely occur, a possible impact on the user,communication device, and the environment of the one or more emergencysituations. In further embodiments, the communication device furtherkeeps the EDC and/or EMS updated about the one or more emergencysituations, and if there is a change in the possible time, or thepossible impact, before the occurrence of the one or more emergencysituations, and if the impact on the user of the communication devicewill be different from that indicated in a last update received by theEDC and/or EMS, about the one or more emergency situations. In someembodiments, the communication device establishes a data connectionbetween the EMS and/or EDC, and the communication device autonomouslyand actively manages the data connection, and, upon sensing that theconnection is severed, attempts to re-establish the data connectionusing an alternate data route between the communication device and acommunication device of the EMS and/or EDC. In some embodiments, thecommunication device hosts an application client and communicates withthe EMS and/or EDC via the application client, the application clientanalyzing the meta-data about the user, communication device, and theenvironment, generating a conclusion from this meta-data, making adecision about the status of the user from this analysis, constructing arequest for emergency assistance, and sending the request for emergencyassistance to an EDC and/or EMS, the application client further managingdata connections, and meta-data information transfers between thecommunication device and the EMS and/or EDC.

In another aspect, disclosed herein are mobile communication devicesconfigured to determine, based on available data, if a user is in needof, or possibly going to need, emergency assistance, and generate andcommunicate a request for emergency assistance, the communicationsdevice comprising: a user interface; physical interaction components; alocation determination module; a communications module configured tosend and receive messages, including a request for emergency assistancecontaining meta-data about the user, communication device and anenvironment around the user and the communication device, and ananalysis of the meta-data, over a communications network; and aprocessor configured to: receive an indication of a location of thecommunication device from the location determination module; analyzemeta-data information as provided to the processor by the communicationmodule of the device, sensors on the device, user input via the userinterface, the location determination module, the meta-data containinginformation about the user, communication device, and the environment,to determine whether or not the user of the communication device is in acurrent situation that requires emergency assistance; upon adetermination that the user of the communication device is in need ofemergency assistance, construct a request for emergency assistance basedon a current status of the user derived from the meta-data available tothe processor, and send the request, along with relevant meta-data, asdetermined by the processor, and conclusions derived from the meta-databy the processor, to the communications module of the communicationdevice; establish a data communications link, via the communicationsmodule, to an EMS and/or EDC; and receive a real-time data from thesensors and locationing module on the communication device, regardinglatest sensed health status and location of the user, and update therequest for emergency assistance based on this information. In someembodiments, the mobile communication device is one of a Tabletcomputer, a smart phone, a laptop computer, a wearable device, or anyother form of end-device used by a user. In further embodiments, thecommunication device hosts an application client and the processorinteracts with the user of the device using the application client, andthe application client is used to translate commands and emergencyalerts from the processor into user recognizable actions. In furtherembodiments, wherein the mobile communication device is furtherconfigured to generate emergency alerts in a form of one or more of avoice command, video data, a text based message, or any other form ofuser-machine interaction that is understandable by a user. In furtherembodiments, the application client translates user input, includinguser responses to an emergency alert presented by the processor to theuser on the user interface into machine code or commands the processorof the device can understand, and sends them to the processor. Infurther embodiments, the processor of the communication devicecommunicates with processors of other communication devices, via thecommunication module of the communication device, and exchangesmeta-data relevant to the emergency situation being responded to thatthe user of the communication device may be involved in, or any criticalinformation about the user, the communication device, or theenvironment, that is permitted, by the user, to be shared with othercommunication devices, and based on the received meta-data informationupdates an existing request for emergency assistance, or constructs anew request for emergency assistance, either autonomously or inconjunction with processors of other communication devices, and relaysthis information to the user and sends the request for emergencyassistance to an EDC and/or EMS via the communication module of thecommunication device.

FIG. 9 illustrates one embodiment of an emergency prediction system(EPS) for autonomously predicting emergencies involving a user'scommunication devices. As shown, a user 900 may be the primary user ofseveral communication devices including a smart phone 906, a laptop ortablet 946 and an IoT device 912 (e.g., a smoke detector in a smarthome, a crash detection sensor in a vehicle). The device 906 maycommunicate with and share data with the other devices 946, 912 usingone or more communication links 922, 952. In some embodiments, thedevice 906 may obtain sensor data from IoT device 912 and laptop 946with information about the user 900 or his or her environment for makingemergency predictions.

The device 906 may include a touchscreen 913 (which may function as adisplay and user interface). The device 906 may also include a computerprogram 908, which may include one or more modules of an emergencyprediction program. Thus, the program 908 may detect or collectinformation about the user through device 906 and provide notificationto the user 900 about pertinent threats and may also connect the user900 to a dispatch center (e.g., EDC 950 or a private dispatch center)for emergency assistance.

In some embodiments, the emergency prediction system includes an EMS930, which optionally connects the user 900 through devices 906, 912,946 when there is an on-going emergency or a possible emergency. Asshown, in some embodiments, the devices 906, 912, 946 connect to the EMS930 through various wired or wireless connections such as cellular voicenetwork, cellular data network, Wi-Fi, Bluetooth®, Internet-basednetworks, etc. For example, in one embodiment, the communication link924 connects device 906 to the EMS 930, while the communication links926 or 945 and 947 connect to the EDC 950 via a gateway 944. In someembodiments, the devices 906, 912, 946 collectively analyze theinformation about the user and environment to determine whether there isan on-going or possible emergency. In some embodiments, one device(e.g., device 906) is a master device that may determine whether thereis an emergency and autonomously decide to send an emergency alert to adispatch center for assistance.

In some embodiments, the emergency prediction modules on the device 906communicate with an emergency prediction server 985 in the EMS 930 wherethe analysis for emergency predictions are conducted. In otherembodiments, the emergency predictions are generated on a predictionserver 1485 (as depicted in FIG. 14A). In some embodiments, theemergency predictions are generated on the device 906. Databases 995(e.g., Master DB, Batch Serving DB, Real-time Serving DB) and othercomponents of the emergency prediction system are housed in the EMS 930.

In some embodiments, the devices 906, 912, 946 are in the same ordifferent locations and the location data for the emergency may beshared with the EDC 950, where a dispatcher 966, a manager (not shown),or another personnel may be informed about the on-going emergency orpossible emergency. In some embodiments, a computer system 981 with anemergency prediction program 985 is accessible to the dispatcher 966 ormanager or other emergency personnel at the EDC 950. In someembodiments, the computer system 981 (e.g., a PSAP system with hardwareand software components as depicted in FIG. 14D) includes a display oruser interface 987 and an emergency prediction program 982. In someembodiments, emergency personnel receive notification about on-going andpossible emergencies and allocation of emergency resources.

In some embodiments, the communication device 906 collects data fromvarious devices including user input for analyzing whether there is anon-going or possible emergency for user 900. Based on this analysis, theEPS determines whether or not the user 900 requires emergencyassistance. In some embodiments, the EPS determines the level of threatto the user 900 if possible emergency is determined including the typeof threat, the possible impact of the threat to the user 900, and theenvironment, and the ways in which the threat can be remedied (if any).

Responsive to the determination by the EPS that the user 900 is in an,or a potential, threat, and after successfully generating andtransmitting a request for emergency assistance to an EMS 930, positiveaffirmation of receipt of the request by the EMS 930 and/or EDC 950 isreceived, in some embodiments. In some embodiments, upon receiving apositive affirmation, the EMS 930 maintains the communication sessionthe EDC 950 and user 900 with updated information and changes to thethreat level.

FIG. 10 illustrates one embodiment of an emergency prediction system forautonomously predicting emergencies involving a group of users. Asshown, a group of users, includes user 1000 with communication device1006, user 1005 with associated device 1007 and user 1010 withassociated device 1016. For instance, the group of users may have agreedto share data with each other and keep an eye on each other. In someembodiments, the users have authorized a group member (e.g., a family orfriend) to make an emergency call in case of an emergency.

In some embodiments, the group of devices communicate with each otherthrough peer-to-peer connections such as shortwave radio connections,Bluetooth® connections, etc. In such embodiments, the devices are ableto communicate with devices in their vicinity. In some embodiments, thegroup of devices are able to communicate even when far away throughinternet-based or cellular connections.

In some embodiments, the device 1006 communicates with and shares datawith the other devices 1007, 1016 about the user 1000 and his or herenvironment. In some embodiments, the devices shares data periodicallyor when there has been a trigger. In some embodiments, the device 1006includes a touchscreen 1012 (which may function as a display and userinterface). In some embodiments, the device 1006 also includes acomputer program 1008, which often includes one or more modules of anemergency prediction program. Thus, in some embodiments, the program1008 detects or collects information about users 1000, 1005, 1010through member devices 1006, 1007, 1016 and provides notification to theusers 1000, 1005, 1010 about pertinent threats affecting other membersof the group. In some embodiments, the EPS connects the users 1000,1005, 1010 to a dispatch center (e.g., EDC 1050 or a private dispatchcenter) for emergency assistance.

In some embodiments, the emergency prediction system includes an EMS1030, which connects users 1000, 1005, 1010 through devices 1006, 1007,1016 when there is an on-going emergency or a possible emergencyaffecting another group member. As shown, devices 1006, 1007, 1016optionally connect to the EMS 1030 through various wired or wirelessconnections such as cellular voice network, cellular data network,Wi-Fi, Bluetooth®, Internet-based networks, etc. For example, in someembodiments, communication link 1024 connects device 1006 to the EMS1030, while the communication links 1026 or 1045 and 1047 connect to theEDC 1050 via a gateway 1044.

In some embodiments, the devices 1006, 1007, 1016 collectively analyzesthe information about the user and environment to determine whetherthere is an on-going or possible emergency. In some embodiments, onedevice (e.g., device 1006) is a master device that determines whetherthere is an emergency and autonomously decide to send an emergency alertto a dispatch center for assistance on behalf of another device or groupmember.

In some embodiments, the emergency prediction modules on the device 1006communicate with an emergency prediction server 1085 in the EMS 1030where the analysis for emergency predictions are conducted. In otherembodiments, the emergency predictions are generated on a predictionserver 1485 (as depicted in FIG. 14A). In some embodiments, theemergency predictions are generated on the device 1006. Databases 1095(e.g., Master DB, Batch Serving DB, Real-time Serving DB) and othercomponents of the emergency prediction system are housed in the EMS1030.

In some embodiments, the devices 1006, 1007, 1016 are in the same ordifferent locations and the location data for the emergency is sharedwith the EDC 1050, where a dispatcher 1066, a manager (not shown), oranother personnel may be informed about the on-going emergency orpossible emergency. In some embodiments, as shown a computer system 1081with an emergency prediction program 1085 is accessible to thedispatcher 1066 or manager or other emergency personnel at the EDC 1050.The computer system 1081 (e.g., a PSAP system with hardware and softwarecomponents as depicted in FIG. 14D). As shown, the system 1081 mayinclude a display or user interface 1087 and an emergency predictionprogram 1082. In some embodiments, on the system 1081, emergencypersonnel receive notification about on-going and possible emergenciesand allocation of emergency resources.

In some embodiments, the communication device 1006 collects data fromvarious devices including user input for analyzing whether there is anon-going or possible emergency for user 1000. Based on this analysis,the EPS determines whether or not users 1000, 1005, 1010 requireemergency assistance. In some embodiments, the EPS determines the levelof threat to the users 1000, 1005, 1010 including the type of threat,the possible impact of the threat to the user, others and theenvironment, and the ways in which the threat can be remedied (if any).

Responsive to the determination by the EPS that one or more users 1000,1005, 1010 are in, or a potential, threat, and after successfullygenerating and transmitting a request for emergency assistance to an EMS1030, positive affirmation of receipt of the request by the EMS 1030and/or EDC 1050 may be received. Upon receiving a positive affirmation,the EMS 1030 often maintains the communication session the EDC 1050 andusers 1000, 1005, 1010 with updated information and changes to thethreat level.

FIG. 11 depicts a method for real-time or near real-time emergencyprediction and notification. The communication device (e.g., device1006, the EMS 1030 in FIG. 10) detects and collects data about one ormore users (e.g., user 1000) and their environment from various devices(act 1112). In some embodiments, the data about the user includes thehealth status of one or more users (e.g., health history, sensed dataincluding heart-beat, heart-rate, blood oxygen levels, pulse-rate). Insome embodiments, the data associated with the environment of the useris also analyzed (act 1114) (e.g., air pressure, oxygen content in theair, carbon dioxide or carbon monoxide levels) and device information(e.g., current GPS position, current hybridized location using locationservices, historical location information, nearest cell tower, celltower triangulation information, Wi-Fi positioning, phone number, deviceID, MAC address, IP address). In some embodiments, threats are detectedbased on detection of sensed data above or below a set threshold range(e.g., car alarm has been triggered). If no imminent or on-going threatis indicated (act 1114), the analysis is repeated after a specifiedperiod of time (act 1136).

If there is a possible, imminent or on-going threat to one or more users(act 1114), there may be an existing or ongoing emergency communicationregarding the user (act 1116). For example, sometimes, there is anon-going emergency call or incident relating to the user. In someembodiments, the device detects an on-going emergency call or sessionand/or the EMS searches through current call or incident data. In someembodiments, upon determining that there is an on-going emergencycommunication (act 1116), the need for notification for the affecteduser, group members, emergency contacts are evaluated (act 1122). Insome embodiments, notifications about on-going or potential emergencyare sent to the affected user, group members, emergency contact based onuser preferences (act 1134). In addition, the need to contact thedispatch center and emergency resource management is evaluated (act1124). In some embodiments, for contacting the dispatch center, theappropriate dispatch center for the type and location of the emergencyis identified (act 1126). If needed, a request for assistance ornotifications are sent to the appropriate dispatch center or emergencyresource managers (act 1126, 1128, 1132).

In some embodiments, when a potential emergency is detected, emergencypredictions are generated to evaluate the level of the threat (act1118). In some embodiments, emergency predictions are generated on aprediction server (such as anomaly detection in real-time as describedin FIGS. 6A and 6B). In some embodiments, when a potential emergency isdetected, the type of emergency and the priority of the emergency aredetermined to determine if notifications to the user, group members besent (act 1122). In some embodiments, notifications about on-going orpotential emergency are sent to the affected user, group members,emergency contact based on user preferences (act 1134). In addition, theneed to contact the dispatch center and emergency resource management isevaluated (act 1124). In some embodiments, for contacting the dispatchcenter, the appropriate dispatch center for the type and location of theemergency is identified (act 1126). If needed, a request for assistanceor notifications are sent to the appropriate dispatch center oremergency resource managers (act 1126, 1128, 1132). In some embodiments,for on-going and potential emergencies, the situation is monitored andcommunications with dispatch center is managed (act 1132).

Emergency Auto-Detection by a Group of Devices

In some aspects, disclosed herein are methods for a group ofcommunication devices to cooperatively act in response to a user of acommunication device in the group of communication devices being in needof emergency assistance, the method comprising: collectively andautonomously determining, by the group of communication devices, whetherthe user is facing an emergency situation and requires emergencyassistance; responsive to determining that the user is in need ofemergency assistance, collectively selecting, by the group ofcommunication devices, one or more communication devices in the group ofcommunication devices that will send a request for emergency assistanceon behalf of all communication devices in the group of communicationdevices that are likely to be impacted by the emergency situation to anEDC and/or an EMS and manage communication between the group ofcommunication devices and the EDC and/or EMS responding to the requestfor emergency assistance; constructing the request for emergencyassistance; sending the request for emergency assistance from the one ormore communication devices to an EDC and/or EMS serving an area wherethe group of communication devices are located; and actively managingcommunication of meta-data information and conclusions derived fromanalysis of the meta-data information between communication devices inthe group of communication devices and/or between the one or morecommunication devices and the EDC and/or EMS. In some embodiments, thedetermination of whether the user requires emergency assistance is madeby the group of communication devices based on data regarding the user,data about the environment around the user, and information about thegroup of communication devices. In some embodiments, the method furthercomprises determining a type of potential threat to the user. In furtherembodiments, the method further comprises determining a possible impactof the potential threat to the user. In further embodiments, the methodfurther comprises determining possible ways in which the threat can beremedied. In further embodiments, the method further comprisesdetermining potential sources of help available near the user. In someembodiments, the request for emergency assistance is constructed basedon an understanding of a current status of the user derived frommeta-data available about the user, the group of communication devices,and an environment around the user. In some embodiments, the methodfurther comprises providing the EMS and/or EDC with data that thedecision to generate the request for emergency assistance was based on.In some embodiments, the method further comprises including meta-datainformation regarding the user and the emergency situation in therequest for emergency assistance. In some embodiments, the methodfurther comprises, responsive to determining at the one or morecommunication devices, after sending the request for emergencyassistance, that the request for emergency assistance has not beenresponded to, resending the request for emergency assistance until asuccessful acknowledgement is received from the EMS and/or the EDC. Infurther embodiments, resending the request for emergency assistancecomprises sending the request for emergency assistance to the same orEDC and/or EMS to which the request for emergency assistance waspreviously sent. In further embodiments, resending the request foremergency assistance comprises sending the request for emergencyassistance to a different EDC and/or EMS that the EDC and/or EMS towhich the request for emergency assistance was previously sent. Infurther embodiments, resending the request for emergency assistancecomprises sending the request for emergency assistance, by the one ormore communication devices that previously sent the request foremergency assistance. In further embodiments, resending the request foremergency assistance comprises sending the request for emergencyassistance, by a different one or more communication devices than theone or more communication devices that previously sent the request foremergency assistance. In some embodiments, the method further comprises:responsive to determining at the one or more communication devices thatthere is additional information available about the emergency situationin addition to information already shared with the users, analyzing theadditional information along with existing meta-data information aboutthe user, the group of communication devices, and the environment aroundthe user; and responsive to determining that the analysis yields adifferent conclusion about a health status of the user than a healthstatus of the user previously shared with the users, sharing thedifferent conclusion with the users. In further embodiments, the EDCand/or EMS performs the act of analyzing the additional informationalong with existing meta-data information about the user, the group ofcommunication devices, and the environment around the user. In furtherembodiments, the one or more communication devices performs at least aportion of the act of analyzing the additional information along withexisting meta-data information about the user, the group ofcommunication devices, and the environment around the user. In yetfurther embodiments, the EDC and/or EMS performs the act of sharing thedifferent conclusion with the users. In some embodiments, the methodfurther comprises, responsive to determining by the group ofcommunication devices that the user is in need of emergency assistance,raising an alarm at interfaces on the communication devices. In someembodiments, the group of communication devices make a prediction, basedon available meta-data about users of the group of communicationdevices, the group of communication devices, and an environment aroundthe users, as to whether or not a first sub-group of the users willlikely be facing a future emergency situation, a likely type of thefuture emergency situation, a likely time before the future emergencysituation will occur, and a likely impact of the future emergencysituation on the first sub-group of the users. In further embodiments, asecond sub-group of the users are currently facing the future emergencysituation, and the method further comprises: sending a request foremergency assistance to the EDC and/or EMS on behalf of the secondsubgroup of users; and keeping the EDC and/or EMS updated about a statusof the first sub-group of users and any changes in the likely timebefore the future emergency situation will occur and/or likely impact ofthe future emergency situation on the first sub-group of the users. Insome embodiments, the group of communication devices make a decision toselect more than one communication device from the group ofcommunication devices to represent and send a request for emergencyassistance on behalf of the group of communication devices, and the morethan one communication device manages communication between the EMSand/or EDC and the group of the communication devices. In someembodiments, individual devices in the group of communication deviceshost an application client and communicate with the EMS and/or EDC viathe application client, the application client including functionalityto: analyze meta-data about users of the group of communication devices,the group of communication devices, and an environment around the users;generate a conclusion from the meta-data about the users, the group ofcommunication devices, and the environment around the users; make adecision about a status of the one or more of the users based on theconclusion; and share the decision with the group of communicationdevices. In further embodiments, the application client constructs therequest for emergency assistance, sends the request for emergencyassistance to the EDC and/or EMS, and manages data connections and themeta-data information communicated between the group of communicationdevices. In some embodiments, the group of communication devices,responsive to determining that the one or more communication devices isnot responsive, select another communication device from the group ofcommunication devices to represent and send the request for emergencyassistance on behalf of the group of communication devices.

In another aspect, disclosed herein are mobile user communicationsdevices configured to participate in a group of communication devices,the mobile user communications device comprising: a user interface; alocation determination module; a communications module configured tosend and receive messages, including a request for emergency assistancecontaining meta-data about communication devices in the group ofcommunication devices, users of the communication devices, and anenvironment around the users; and a processor configured to: receive anindication of a location of the user mobile communication device fromthe location determination module; receive meta-data about the users,the communication devices, and the environment around the users fromother communication devices of the group of communication devices;analyze the meta-data and the indication of the location to determine ifone or more of the users is in a situation that requires emergencyassistance; responsive to determining that the one or more of the usersis in need of emergency assistance, one of sending the meta-data to aselected communication device of the group of communication devices, orconstructing a request for emergency assistance based on anunderstanding of a current status of the users derived from themeta-data and sending the request, along with any relevant meta-data andany conclusions derived from the meta-data, to the communications moduleof the communication device; and establish a data communications link,via the communications module, to one or more of an EMS, an EDC, orcommunication devices of other communication devices in the group ofcommunication devices. In further embodiments, the mobile communicationdevice is selected from the group including a Tablet computer, a Smartphone, laptop computer, or a wearable device. In further embodiments,the mobile communication device hosts an application client, theprocessor is configured to interact with a user of the mobilecommunication device using the application client, and the applicationclient is configured to translate commands and emergency alerts from theprocessor into a user recognizable action, translate user input intomachine code or commands the processor of the device can understand, andsend the machine code or commands to the processor. In yet furtherembodiments, the user recognizable action includes one or more of adisplay on the user interface, a voice command, video data, or a textbased message. In yet further embodiments, the user input includes userresponses to an emergency alert presented by the processor to the useron the user interface. In further embodiments, the processor isconfigured to communicate with processors of other mobile communicationdevices via the communication module, and exchange meta-data relevant toan emergency situation that the one or more of the users is facing. Inyet further embodiments, the processor is further configured tocommunicate information about the users, the communication devices, andthe environment around the users, with the processors of the othermobile communication devices. In yet further embodiments, the processoris further configured to one of update an existing request for emergencyassistance based on meta-data information received from the other mobilecommunication devices, or construct a new request for emergencyassistance based on the meta-data information received from the othermobile communication devices. In still yet further embodiments, theprocessor is further configured to one of update the existing requestfor emergency assistance or construct the new request for emergencyassistance in conjunction with processors of the other communicationdevices. In still yet further embodiments, the processor is furtherconfigured to relay one of the updated and new requests for emergencyassistance to one or more communication devices selected by the group ofcommunication devices to contact the EDC and/or EMS on behalf of thegroup of communication devices. In still yet further embodiments, theprocessor is further configured to relay one of the updated and newrequests for emergency assistance to an EDC and/or EMS via thecommunication module.

FIG. 12 illustrates one embodiment of an emergency prediction system forautonomously predicting emergencies involving a group of users indifferent PSAP service areas. Analogous to FIG. 10, a group of users,including user 1200 with communication device 1206, user 1205 withassociated device 1207 and user 1210 with associated device 1216. Thegroup of users may have agreed to share data with each other and keep aneye on each other. In some embodiments, the users may have authorized agroup member (e.g., a family or friend) to make an emergency call incase of an emergency.

As shown in FIG. 12, the users may be located in different geographicalareas throughout the country. Here, user 1200 and 1210 are located inZone X while user 1205 is in Zone Y where two different PSAPjurisdictions are responsible for providing for emergency assistance(i.e., EDC 1250 & 1255). In some embodiments, different dispatch centersmay be needed for different types of emergencies (e.g., a police hotlinemay be called if there is a burglary, while a private road-sideassistance may be called when a car breaks down on the highway).

The device 1206 may communicate with and share data with the otherdevices 1207, 1216 (both near and far) about the user 1200 and his orher environment. In some embodiments, the devices may share dataperiodically or when there has been a trigger. The device 1206 mayinclude a touchscreen 1212 (which may function as a display and userinterface). The device 1206 may also include a computer program 1208,which may include one or more modules of an emergency predictionprogram. Thus, the program 1208 may detect or collect information aboutusers 1200, 1205, 1210 through member devices 1206, 1207, 1216 andprovide notification to the users 1200, 1205, 1210 about pertinentthreats affecting other members of the group. In some embodiments, theEPS may connect the users 1200, 1205, 1210 to a dispatch center (e.g.,EDC 1250 or a private dispatch center) for emergency assistance.

The emergency prediction system may include an EMS 1230, which mayconnect users 1200, 1205, 1210 through devices 1206, 1207, 1216 whenthere is an on-going emergency or a possible emergency affecting anothergroup member. As shown, devices 1206, 1207, 1216 may connect to the EMS1030 through various wired or wireless connections such as cellularvoice network, cellular data network, Wi-Fi, Bluetooth®, Internet-basednetworks, etc. For example, in some embodiments, the communication link1224 connects device 1206 to the EMS 1230, while the communication links1226 or 1245 and 1247 connect to the EDC 1250 via a gateway 1244.

The EMS 1230 may play an important role in directing the request foremergency assistance to the appropriate dispatch center (e.g., PSAP).Here, since Zone X is in the jurisdiction of EDC 1250, that is theappropriate PSAP if user 1200 or user 1210 is in an on-going orpotential emergency. However, if user 1205 in Zone Y is in need ofemergency assistance, EDC 1255 should be contacted. If the emergencycommunication is sent to the wrong dispatch center, precious time may bewasted in re-routing and transfer.

For this purpose, the location of the device 1206, 1207 or 1216 may beused as the location of the emergency for determination of theappropriate dispatch center. For the device location, a GPS position,cellular triangulation, hybrid device-based location using locationservices, Wi-Fi positioning, Bluetooth® positioning, use of Wi-Fi and/orBluetooth® access points, etc.

In same way, notifications about potential emergency predictionsconcerning user 1200 and 1210 should be directed to EDC 1250 forresource planning, while for user 1205 to EDC 1255.

In some embodiments, the devices 1206, 1207, 1216 may collectivelyanalyze the information about the user and environment to determinewhether there is an on-going or possible emergency. In some embodiments,one device (e.g., device 1206) may be a master device that may determinewhether there is an emergency and autonomously decide to send anemergency alert to a dispatch center for assistance on behalf of anotherdevice or group member.

In some embodiments, the emergency prediction modules on the device 1206may communicate with an emergency prediction server 1285 in the EMS 1230where the analysis for emergency predictions are conducted. Databases1295 (e.g., Master DB, Batch Serving DB, Real-time Serving DB) and othercomponents of the emergency prediction system may also be housed in theEMS 1230.

The devices 1206, 1207, 1216 may be in the same or different locationsand the location data for the emergency may be shared with the EDC 1250,where an emergency personnel (e.g., dispatcher 1266, a manager (notshown)) may be informed about the on-going emergency or potentialemergency. As shown a computer system 1281 with an emergency predictionprogram 1285 may be accessible to the dispatcher 1266 or manager orother emergency personnel at the EDC 1250. At EDC 1255, a dispatcher1268 may have access to computer system 1289.

The computer systems 1281, 1289 (e.g., a PSAP system with hardware andsoftware components as depicted in FIG. 14D). As shown, the systems1281, 1289 may include display or user interfaces 1087, 1283 s and anemergency prediction program 1282. On the systems 1281, 1289, emergencypersonnel may get notification about on-going and potential emergenciesand allocation of emergency resources.

FIG. 13 depicts a method for sending a request for assistance based on athreat of an emergency involving a group of users. A group ofcommunication devices may detect, on an autonomous basis, if one or moreusers is in need, or will potentially be in need of emergency assistancewithin a certain period of time, and communicate this information to theappropriate dispatch center.

In accordance with the method, a communication device detects data aboutthe user, associated communication devices and the environment aroundthe user 100 (act 1312). The communication device may share the datawith the other communication devices in a group of devices (act 1314).The group of communication devices may collectively or individually(through use of a prediction server) perform an analysis of on-going orpotential emergencies that may be affecting the user or his or herenvironment or property (act 1316).

Responsive to detecting that there is no pertinent (act 1316), data isupdated to the group (act 1318). If a threat is detected a communicationdevice is chosen (or pre-determined) for sending a request for emergencyassistance on behalf of all the other communication devices in the group(act 1324, 1326, 1328).

If the group of communication devices 101, 102 derive a collectivedecision that there is a threat, the need to contact a dispatch center(e.g., an EDC) is evaluated periodically (act 1334, 1332). Responsive todeciding that a request for emergency assistance should be sent (act1322) the communication device may determine if there is an on-goingcall for emergency assistance (act 1334). Responsive to detecting thatthere is an on-going communication (act 1334), an updated request foremergency assistance (act 1336). Alternately when there is no on-goingcommunication (act 1334), a request for assistance is sent (act 1338).The communication device or EMS may verify that the request wassuccessfully transmitted to the appropriate dispatch center beforemaximum number retries have been exhausted and the communication withuser and dispatch center is managed (act 1342, 1344, 1346).

FIG. 14B also shows a schematic diagram of one embodiment of anemergency management system 1430 as described herein. In someembodiments, the emergency management system 1430 comprises one or moreof an operating system 1432, at least one central processing unit orprocessor 1434, a memory unit 1436, a communication element 1438, and asoftware application 1448 (e.g., server application) with softwaremodule 1449. In some embodiments, the emergency management system 1430comprises one or more databases 1495 for generating emergencypredictions. In some embodiments, the emergency management system 1430comprises a master database 1454, batch serving database 1458, and areal-time serving database 1468.

FIG. 14C shows a schematic diagram of one embodiment of a softwareapplication 1428 installed on a device. In some embodiments, thesoftware application 1428 comprises one or more device software modulesselected from a group module 1419, a prediction module 1421, a datasharing module 1423, a proxy communication module 1425, an alert module1427, a status notification module 1429, a location determination module133, or any combination thereof. In some embodiments, the softwareapplication 1448 comprises one or more device software modules selectedfrom an emergency communication module 1441, a proxy determinationmodule 1443, an emergency management module 1445, a group locationmodule 1447, or any combination thereof. In some embodiments, thecomputer program 1488 comprises one or more device software modules

FIG. 14C also shows a schematic diagram of one embodiment of a serverapplication 1448 installed on a server (e.g., a server in an EMS). Insome embodiments, the server application 1448 comprises one or moreserver software modules from an emergency communication module 1441, aproxy determination module 1443, an emergency management module 1445, agroup location module 1447, or any combination thereof.

FIG. 14C also shows a schematic diagram of one embodiment of anemergency prediction program 1488 installed on a server (e.g., a serverin an EMS). In some embodiments, the server application 1488 comprisesone or more emergency prediction software modules selected from aspatiotemporal communication module 1420, an augmenting module 1410, ananomaly module 1440, a resource allocation module 1470, or anycombination thereof.

FIGS. 10 and 12 illustrate an embodiment of proxy calling where a user1000, 1200 of a first communication device 1006, 1206 sends an emergencyalert on behalf of a second device 1007, 1207 (or a user thereof 1005,1205). In some embodiments, a second device 1007, 1207 is a memberdevice in a group of devices, wherein member devices in the group areauthorized to share data. In some embodiments, a user 900 of acommunication device 906 initiates the process to send a request forassistance to an EDC 955 on behalf of a user 905 of the second device907. In some embodiments, the user 905 has authorized the second device907 to share his or her location with the user 900. In some embodiments,user 900 and 905 are in a group of family and/or friends who have joinedtheir devices to a group of devices and authorized sharing theirlocation data with each other.

In some embodiments, the communication device 1006 includes a computerprogram 1008, such as, for example, a software application 1428 as shownin FIG. 14C. In some embodiments, a user 1000 interacts with thecommunication device 1006 using the user interface 1012 (e.g., soft keyson a touch screen, press or tap buttons on the front or sides of thedevice 1006). In some embodiments, user 1200 and device 1206 are locatedin “zone X”, which is a geographical area that is within thejurisdiction of an EDC 1250, such as a Public Safety Answering Point(PSAP).

FIG. 14D shows a schematic diagram of one embodiment of a Public SafetyAnswering Point (PSAP) system 1451 as described herein. In someembodiments, the PSAP system 1451 comprises one or more of display 1457,a user interface 1453, at least one central processing unit or processor1454, a memory unit 1456, a network component 1462, an audio system 1462(e.g., microphone, speaker and/or a call-taking headset) and a computerprogram such as a PSAP Emergency Prediction Application 1482. In someembodiments, the PSAP application 1482 comprises one or more softwaremodules 1459. In some embodiments, the PSAP system 1451 comprises adatabase of responders 1477 (not shown), such as medical assets, policeassets, fire response assets, rescue assets, safety assets, etc.

FIG. 14D also shows a schematic diagram of one embodiment of a PSAPapplication 1482 installed on a PSAP system 1451 (e.g., a server in thePSAP system). In some embodiments, the PSAP application 1482 comprisesone or more prediction software modules. In some embodiments, a softwaremodule is a spatiotemporal emergency prediction module 1463, anaugmenting emergency data module 1465, an anomaly detection module 1467,an emergency resource allocation module 1469 or an emergency data module1471.

Digital Processing Device

In some embodiments, the platforms, media, methods and applicationsdescribed herein include a digital processing device, a processor, oruse of the same. In further embodiments, the digital processing deviceincludes one or more hardware central processing units (CPU) that carryout the device's functions. In still further embodiments, the digitalprocessing device further comprises an operating system configured toperform executable instructions. In some embodiments, the digitalprocessing device is optionally connected a computer network. In furtherembodiments, the digital processing device is optionally connected tothe Internet such that it accesses the World Wide Web. In still furtherembodiments, the digital processing device is optionally connected to acloud computing infrastructure. In other embodiments, the digitalprocessing device is optionally connected to an intranet. In otherembodiments, the digital processing device is optionally connected to adata storage device.

In accordance with the description herein, suitable digital processingdevices include, by way of non-limiting examples, server computers,desktop computers, laptop computers, notebook computers, sub-notebookcomputers, netbook computers, netpad computers, set-top computers,handheld computers, Internet appliances, mobile smartphones, walkietalkies, radios, tablet computers, personal digital assistants, videogame consoles, and vehicular consoles. Those of skill in the art willrecognize that many smartphones are suitable for use in the systemdescribed herein. Those of skill in the art will also recognize thatselect televisions, video players, and digital music players withoptional computer network connectivity are suitable for use in thesystem described herein. Suitable tablet computers include those withbooklet, slate, and convertible configurations, known to those of skillin the art.

In some embodiments, the digital processing device includes an operatingsystem configured to perform executable instructions. The operatingsystem is, for example, software, including programs and data, whichmanages the device's hardware and provides services for execution ofapplications. Those of skill in the art will recognize that suitableserver operating systems include, by way of non-limiting examples,FreeBSD, OpenBSD, NetBSD®, Linux, Apple® Mac OS X Server®, Oracle®Solaris®, Windows Server®, and Novell® NetWare®. Those of skill in theart will recognize that suitable personal computer operating systemsinclude, by way of non-limiting examples, Microsoft® Windows®, Apple®Mac OS X®, UNIX®, and UNIX-like operating systems such as GNU/Linux®. Insome embodiments, the operating system is provided by cloud computing.Those of skill in the art will also recognize that suitable mobile smartphone operating systems include, by way of non-limiting examples, Nokia®Symbian® OS, Apple® iOS®, Research In Motion® BlackBerry OS®, Google®Android®, Microsoft® Windows Phone® OS, Microsoft® Windows Mobile® OS,Linux®, and Palm® WebOS®.

In some embodiments, the device includes a storage and/or memory device.The storage and/or memory device is one or more physical apparatusesused to store data or programs on a temporary or permanent basis. Insome embodiments, the device is volatile memory and requires power tomaintain stored information. In some embodiments, the device isnon-volatile memory and retains stored information when the digitalprocessing device is not powered. In further embodiments, thenon-volatile memory comprises flash memory. In some embodiments, thenon-volatile memory comprises dynamic random-access memory (DRAM). Insome embodiments, the non-volatile memory comprises ferroelectric randomaccess memory (FRAM). In some embodiments, the non-volatile memorycomprises phase-change random access memory (PRAM). In some embodiments,the non-volatile memory comprises magnetoresistive random-access memory(MRAM). In other embodiments, the device is a storage device including,by way of non-limiting examples, CD-ROMs, DVDs, flash memory devices,magnetic disk drives, magnetic tapes drives, optical disk drives, andcloud computing based storage. In further embodiments, the storageand/or memory device is a combination of devices such as those disclosedherein.

In some embodiments, the digital processing device includes a display tosend visual information to a subject. In some embodiments, the displayis a cathode ray tube (CRT). In some embodiments, the display is aliquid crystal display (LCD). In further embodiments, the display is athin film transistor liquid crystal display (TFT-LCD). In someembodiments, the display is an organic light emitting diode (OLED)display. In various further embodiments, on OLED display is apassive-matrix OLED (PMOLED) or active-matrix OLED (AMOLED) display. Insome embodiments, the display is a plasma display. In some embodiments,the display is E-paper or E ink. In other embodiments, the display is avideo projector. In still further embodiments, the display is acombination of devices such as those disclosed herein.

In some embodiments, the digital processing device includes an inputdevice to receive information from a subject. In some embodiments, theinput device is a keyboard. In some embodiments, the input device is apointing device including, by way of non-limiting examples, a mouse,trackball, track pad, joystick, game controller, or stylus. In someembodiments, the input device is a touch screen or a multi-touch screen.In other embodiments, the input device is a microphone to capture voiceor other sound input. In other embodiments, the input device is a videocamera or other sensor to capture motion or visual input. In furtherembodiments, the input device is a Kinect, Leap Motion, or the like. Instill further embodiments, the input device is a combination of devicessuch as those disclosed herein.

Non-Transitory Computer Readable Storage Medium

In some embodiments, the platforms, media, methods and applicationsdescribed herein include one or more non-transitory computer readablestorage media encoded with a program including instructions executableby the operating system of an optionally networked digital processingdevice. In further embodiments, a computer readable storage medium is atangible component of a digital processing device. In still furtherembodiments, a computer readable storage medium is optionally removablefrom a digital processing device. In some embodiments, a computerreadable storage medium includes, by way of non-limiting examples,CD-ROMs, DVDs, flash memory devices, solid state memory, magnetic diskdrives, magnetic tape drives, optical disk drives, cloud computingsystems and services, and the like. In some cases, the program andinstructions are permanently, substantially permanently,semi-permanently, or non-transitorily encoded on the media.

Computer Program

In some embodiments, the platforms, media, methods and applicationsdescribed herein include at least one computer program, or use of thesame. A computer program includes a sequence of instructions, executablein the digital processing device's CPU, written to perform a specifiedtask. Computer readable instructions may be implemented as programmodules, such as functions, objects, Application Programming Interfaces(APIs), data structures, and the like, that perform particular tasks orimplement particular abstract data types. In light of the disclosureprovided herein, those of skill in the art will recognize that acomputer program may be written in various versions of variouslanguages.

The functionality of the computer readable instructions may be combinedor distributed as desired in various environments. In some embodiments,a computer program comprises one sequence of instructions. In someembodiments, a computer program comprises a plurality of sequences ofinstructions. In some embodiments, a computer program is provided fromone location. In other embodiments, a computer program is provided froma plurality of locations. In various embodiments, a computer programincludes one or more software modules. In various embodiments, acomputer program includes, in part or in whole, one or more webapplications, one or more mobile applications, one or more standaloneapplications, one or more web browser plug-ins, extensions, add-ins, oradd-ons, or combinations thereof.

Web Application

In some embodiments, a computer program includes a web application. Inlight of the disclosure provided herein, those of skill in the art willrecognize that a web application, in various embodiments, utilizes oneor more software frameworks and one or more database systems. In someembodiments, a web application is created upon a software framework suchas Microsoft® .NET or Ruby on Rails (RoR). In some embodiments, a webapplication utilizes one or more database systems including, by way ofnon-limiting examples, relational, non-relational, object oriented,associative, and XML database systems. In further embodiments, suitablerelational database systems include, by way of non-limiting examples,Microsoft® SQL Server, mySQL™, and Oracle®. Those of skill in the artwill also recognize that a web application, in various embodiments, iswritten in one or more versions of one or more languages. A webapplication may be written in one or more markup languages, presentationdefinition languages, client-side scripting languages, server-sidecoding languages, database query languages, or combinations thereof. Insome embodiments, a web application is written to some extent in amarkup language such as Hypertext Markup Language (HTML), ExtensibleHypertext Markup Language (XHTML), or eXtensible Markup Language (XML).In some embodiments, a web application is written to some extent in apresentation definition language such as Cascading Style Sheets (CSS).In some embodiments, a web application is written to some extent in aclient-side scripting language such as Asynchronous Javascript and XML(AJAX), Flash® Actionscript, Javascript, or Silverlight®. In someembodiments, a web application is written to some extent in aserver-side coding language such as Active Server Pages (ASP),ColdFusion®, Perl, Java™, JavaServer Pages (JSP), Hypertext Preprocessor(PHP), Python™, Ruby, Tcl, Smalltalk, WebDNA®, or Groovy. In someembodiments, a web application is written to some extent in a databasequery language such as Structured Query Language (SQL). In someembodiments, a web application integrates enterprise server productssuch as IBM® Lotus Domino®. In some embodiments, a web applicationincludes a media player element. In various further embodiments, a mediaplayer element utilizes one or more of many suitable multimediatechnologies including, by way of non-limiting examples, Adobe® Flash®,HTML 5, Apple® QuickTime®, Microsoft® Silverlight®, Java™, and Unity®.

Mobile Application

In some embodiments, a computer program includes a mobile applicationprovided to a mobile digital processing device. In some embodiments, themobile application is provided to a mobile digital processing device atthe time it is manufactured. In other embodiments, the mobileapplication is provided to a mobile digital processing device via thecomputer network described herein.

In view of the disclosure provided herein, a mobile application iscreated by techniques known to those of skill in the art using hardware,languages, and development environments known to the art. Those of skillin the art will recognize that mobile applications are written inseveral languages. Suitable programming languages include, by way ofnon-limiting examples, C, C++, C#, Objective-C, Java™, Javascript,Pascal, Object Pascal, Python™, Ruby, VB.NET, WML, and XHTML/HTML withor without CSS, or combinations thereof.

Suitable mobile application development environments are available fromseveral sources. Commercially available development environmentsinclude, by way of non-limiting examples, AirplaySDK, alcheMo,Appcelerator®, Celsius, Bedrock, Flash Lite, .NET Compact Framework,Rhomobile, and WorkLight Mobile Platform. Other development environmentsare available without cost including, by way of non-limiting examples,Lazarus, MobiFlex, MoSync, and Phonegap. Also, mobile devicemanufacturers distribute software developer kits including, by way ofnon-limiting examples, iPhone and iPad (iOS) SDK, Android™ SDK,BlackBerry® SDK, BREW SDK, Palm® OS SDK, Symbian SDK, webOS SDK, andWindows® Mobile SDK.

Those of skill in the art will recognize that several commercial forumsare available for distribution of mobile applications including, by wayof non-limiting examples, Apple® App Store, Android™ Market, BlackBerry®App World, App Store for Palm devices, App Catalog for webOS, Windows®Marketplace for Mobile, Ovi Store for Nokia® devices, Samsung® Apps, andNintendo® DSi Shop.

Standalone Application

In some embodiments, a computer program includes a standaloneapplication, which is a program that is run as an independent computerprocess, not an add-on to an existing process, e.g., not a plug-in.Those of skill in the art will recognize that standalone applicationsare often compiled. A compiler is a computer program(s) that transformssource code written in a programming language into binary object codesuch as assembly language or machine code. Suitable compiled programminglanguages include, by way of non-limiting examples, C, C++, Objective-C,COBOL, Delphi, Eiffel, Java™, Lisp, Python™, Visual Basic, and VB .NET,or combinations thereof. Compilation is often performed, at least inpart, to create an executable program. In some embodiments, a computerprogram includes one or more executable compiled applications.

Software Modules

In some embodiments, the platforms, media, methods and applicationsdescribed herein include software, server, and/or database modules, oruse of the same. In view of the disclosure provided herein, softwaremodules are created by techniques known to those of skill in the artusing machines, software, and languages known to the art. The softwaremodules disclosed herein are implemented in a multitude of ways. Invarious embodiments, a software module comprises a file, a section ofcode, a programming object, a programming structure, or combinationsthereof. In further various embodiments, a software module comprises aplurality of files, a plurality of sections of code, a plurality ofprogramming objects, a plurality of programming structures, orcombinations thereof. In various embodiments, the one or more softwaremodules comprise, by way of non-limiting examples, a web application, amobile application, and a standalone application. In some embodiments,software modules are in one computer program or application. In otherembodiments, software modules are in more than one computer program orapplication. In some embodiments, software modules are hosted on onemachine. In other embodiments, software modules are hosted on more thanone machine. In further embodiments, software modules are hosted oncloud computing platforms. In some embodiments, software modules arehosted on one or more machines in one location. In other embodiments,software modules are hosted on one or more machines in more than onelocation.

Databases

In some embodiments, the platforms, systems, media, and methodsdisclosed herein include one or more databases, or use of the same. Inview of the disclosure provided herein, those of skill in the art willrecognize that many databases are suitable for storage and retrieval ofbarcode, route, parcel, subject, or network information. In variousembodiments, suitable databases include, by way of non-limitingexamples, relational databases, non-relational databases, objectoriented databases, object databases, entity-relationship modeldatabases, associative databases, and XML databases. In someembodiments, a database is internet-based. In further embodiments, adatabase is web-based. In still further embodiments, a database is cloudcomputing-based. In other embodiments, a database is based on one ormore local computer storage devices.

Web Browser Plug-in

In some embodiments, the computer program includes a web browserplug-in. In computing, a plug-in is one or more software components thatadd specific functionality to a larger software application. Makers ofsoftware applications support plug-ins to enable third-party developersto create abilities which extend an application, to support easilyadding new features, and to reduce the size of an application. Whensupported, plug-ins enable customizing the functionality of a softwareapplication. For example, plug-ins are commonly used in web browsers toplay video, generate interactivity, scan for viruses, and displayparticular file types. Those of skill in the art will be familiar withseveral web browser plug-ins including, Adobe® Flash® Player, Microsoft®Silverlight®, and Apple® QuickTime®. In some embodiments, the toolbarcomprises one or more web browser extensions, add-ins, or add-ons. Insome embodiments, the toolbar comprises one or more explorer bars, toolbands, or desk bands.

In view of the disclosure provided herein, those of skill in the artwill recognize that several plug-in frameworks are available that enabledevelopment of plug-ins in various programming languages, including, byway of non-limiting examples, C++, Delphi, Java™, PHP, Python™, and VB.NET, or combinations thereof.

Web browsers (also called Internet browsers) are software applications,designed for use with network-connected digital processing devices, forretrieving, presenting, and traversing information resources on theWorld Wide Web. Suitable web browsers include, by way of non-limitingexamples, Microsoft® Internet Explorer®, Mozilla® Firefox®, Google®Chrome, Apple® Safari®, Opera Software® Opera®, and KDE Konqueror. Insome embodiments, the web browser is a mobile web browser. Mobile webbrowsers (also called microbrowsers, mini-browsers, and wirelessbrowsers) are designed for use on mobile digital processing devicesincluding, by way of non-limiting examples, handheld computers, tabletcomputers, netbook computers, subnotebook computers, smartphones, musicplayers, personal digital assistants (PDAs), and handheld video gamesystems. Suitable mobile web browsers include, by way of non-limitingexamples, Google® Android® browser, RIM BlackBerry® Browser, Apple®Safari®, Palm® Blazer, Palm® WebOS® Browser, Mozilla® Firefox® formobile, Microsoft® Internet Explorer® Mobile, Amazon® Kindle® Basic Web,Nokia® Browser, Opera Software® Opera® Mobile, and Sony® PSP™ browser.

While preferred embodiments of the present invention have been shown anddescribed herein, it will be obvious to those skilled in the art thatsuch embodiments are provided by way of example only. Numerousvariations, changes, and substitutions will now occur to those skilledin the art without departing from the invention. It should be understoodthat various alternatives to the embodiments of the invention describedherein may be employed in practicing the invention. It is intended thatthe following claims define the scope of the invention and that methodsand structures within the scope of these claims and their equivalents becovered thereby.

EXAMPLES Example 1—Planning for Staffing and Resource Allocation

An operations officer for a City A fire department is tasked withdeveloping a resource allocation plan for the upcoming week. The purposeof this plan is to report on the available fire resources in the city,identify any possible gaps or risks, and provide recommendations forsolutions to cover identified gaps and mitigate risks. Fire resourcesinclude the number and type of fire vehicles available in the city andtheir base locations.

City A has a data sharing agreement in place with an emergencyprediction system and provides call data from the city's PSAPs on adaily basis for all types of emergency calls. The fire departmentutilizes various software tools to conduct operations planning, and hasintegrated the outputs of the system into a resource planningapplication with a graphical user interface. This application allowsfire department personnel to display and explore information on adashboard that includes tables, charts, and a geographical map annotatedwith multiple layers, such as locations of fire resources, boundaries ofareas of responsibility for each fire station, emergency calls, andpredicted emergency call densities for the city. Filters and selectionoptions are available to display the desired layers with options forviewing detailed or aggregated information and can be used to conductanalysis and produce reports.

The operations officer selects an option on the application to produce arecommended allocation of fire response vehicles for the next week, from00:00 hours on Monday until 23:59 hours on the next Sunday, and todisplay the recommended allocations on the map and in associated tablesfor each day of the week. The emergency prediction system has alreadycalculated ahead of time the predicted density of fire-related emergencycalls for every hour of the next week. The emergency prediction systemruns on a daily schedule providing new predictions and updating currentpredictions. The application makes an API call to the PSAP system torequest the call density data for the week. The results are then sampledand aggregated for each fire station's area of responsibility to give adaily estimate of calls, which is shown on the map as a layer.

The officer recalls that two fire vehicles are in maintenance for theweek, and updates a table that tracks the current availability of firevehicles. The officer then selects an option to calculate the vehicleallocation recommendation. The updated emergency resource data is sentto the emergency prediction system, and the emergency resourceallocation module calculates the recommended allocations. The systemdetects that three areas of the city do not have adequate fire resourcesto respond to the estimated number of calls on Wednesday. These areasare visually highlighted on the map and in a notification table in theapplication. The officer transmits this report to his supervisor andbrings up the identified gaps in coverage for discussion at the weeklyplanning meeting.

Example 2—Group Emergency Detection

An operations officer for the City B police department is monitoringcurrent police operations from the department's tactical operationscenter. The city has a data sharing agreement in place with an emergencyprediction system and provides emergency call data on a daily basis tothe system. The tactical operations center subscribes to the system'sanomalous cluster detection and notification system, and the output isintegrated as a module in the operations center's monitoring dashboard.

A large truck overturns on one of the main streets in the city, blockingtraffic both ways and causing several other vehicles to collide.Individuals nearby begin calling 911 to report the accident. As theemergency calls arrive, they are processed as an incoming data streamfrom the PSAP and augmented with additional information (such as type ofdevice, call duration, etc.) and predictions regarding the call natureand priority. Five calls occur within a minute of the accident, and theemergency event detection module detects the cluster. A notification issent to the tactical operation center dashboard, and is displayed on amap with the probable location of the event based on the location of theassociated calls. The dashboard also displays that the calls arepredicted to be high priority. The operations officer responds to thenotification and begins directing police units to the location of theaccident.

As this large traffic event is taking place, the PSAP is receiving amuch higher number of calls than usual, resulting in calls being placedin a queue to be answered. The software that assists in directing callsfor the PSAP is integrated with the emergency prediction system, andwhen each call arrives, the system detects if it is part of a largercluster. Once the cluster has been identified, the PSAP systemrecognizes that many operators are handling calls from the sameemergency event. A new call arrives that is not part of the cluster, butis predicted to be a high-priority medical call. The systemautomatically moves this call up in the queue, allowing the emergency tobe responded to quickly.

Example 3—Self-Driving Car

City C has begun allowing self-driving cars to operate within the citylimits for an on-demand car service. A taxi service has deployed a fleetof self-driving cars. A command center monitors the cars throughout thecity and sends out roadside assistance when needed. The car's navigationsystem utilizes several data sources to determine the optimal route. Oneof these inputs is emergency event call clusters detected by anemergency prediction system. The car is driving a passenger across townutilizing one of the main roads in city C. During the trip, a largetruck overturns on the route several miles away. As individuals begin tocall 911 to report the accident, our system detects the cluster andrecognizes that it is on the road with the self-driving car based on thelocation of the calls. The self-driving car receives a notification thatan emergency event is taking place along the route, and it recalculatesthe trip to avoid the area of the accident.

Example 4—Data Augmentation for Resource Allocation Planning

An operations officer for a City D fire department is tasked withdeveloping a resource allocation plan for the upcoming week. The purposeof this plan is to report on the available fire resources in the city,identify any possible gaps or risks, and provide recommendations forsolutions to cover identified gaps and mitigate risks. Fire resourcesinclude the number and type of fire vehicles available in the city andtheir base locations.

City D has a data sharing agreement in place with an emergencyprediction system and provides call data from the city's PSAPs on adaily basis for all types of emergency calls. The PSAP call data isunlabeled and stored in a proprietary database operated by the City.Meanwhile, the emergency prediction system maintains a separate databasefor labeled call data for calls routed through an emergency managementsystem (EMS) that manages emergency communication sessions involvingcommunication devices. A subset of the labeled call data correspond to asubset of the PSAP unlabeled call data. In these instances, an emergencycall such as from a user mobile phone is routed through the EMS to thePSAP, but the PSAP does not share an database with the EMS, insteadstoring its own unlabeled call data separately. The unlabeled call dataalso includes emergency response time. Conversely, the EMS stores itslabeled data based on information obtained from the calling device withthe labels including emergency type (fire, police, medical, vehicle).However, the labeled call data does not include emergency response time.Accordingly, the labeled and unlabeled data each includes informationthat the other does not have. In this case, the unlabeled call data fromthe PSAP database is augmented by matching with the correspondinglabeled call data from the EMS. The augmented emergency call data isthen used by the emergency prediction system to train a predictionalgorithm to determine estimated emergency calls relating to fireemergencies.

The fire department utilizes various software tools to conductoperations planning, and has integrated the outputs of the system into aresource planning application with a graphical user interface. Thisapplication allows fire department personnel to display and exploreinformation on a dashboard that includes tables, charts, and ageographical map annotated with multiple layers, such as locations offire resources, boundaries of areas of responsibility for each firestation, emergency calls, and predicted emergency call densities for thecity. Filters and selection options are available to display the desiredlayers with options for viewing detailed or aggregated information andare optionally used to conduct analysis and produce reports.

The operations officer selects an option on the application to produce arecommended allocation of fire response vehicles for the next week, from00:00 hours on Monday until 23:59 hours on the next Sunday, and todisplay the recommended allocations on the map and in associated tablesfor each day of the week. The emergency prediction system has alreadycalculated ahead of time the predicted density of fire-related emergencycalls for every hour of the next week based on the augmented emergencycall data. The emergency prediction system runs on a daily scheduleproviding new predictions and updating current predictions. Theapplication makes an API call to the PSAP system to request the calldensity data for the week. The results are then sampled and aggregatedfor each fire station's area of responsibility to give a daily estimateof calls, which is shown on the map as a layer.

The officer recalls that two fire vehicles are in maintenance for theweek, and updates a table that tracks the current availability of firevehicles. The officer then selects an option to calculate the vehicleallocation recommendation. The updated emergency resource data is sentto the emergency prediction system, and the emergency resourceallocation module calculates the recommended allocations. The systemdetects that three areas of the city do not have adequate fire resourcesto respond to the estimated number of calls on Wednesday. These areasare visually highlighted on the map and in a notification table in theapplication. The officer transmits this report to his supervisor andbrings up the identified gaps in coverage for discussion at the weeklyplanning meeting.

1.-20. (canceled)
 21. A method comprising: detecting anomalous callclusters of emergency calls in a call data stream during a time intervalwithin geographic boundaries; providing a digital map comprisinganomalous call cluster information for detected anomalous call clusters;and displaying the anomalous call cluster information on the digital mapdisplayed by an emergency management system.
 22. The method of claim 21,further comprising: providing the anomalous call cluster informationwithin a geographic boundary comprising a radius.
 23. The method ofclaim 22, further comprising: providing the anomalous call clusterinformation comprising a start time and end time for the time interval,and number of calls within the anomalous call cluster.
 24. The method ofclaim 22, further comprising: providing an expected number of callswithin the geographic boundary.
 25. The method of claim 21, furthercomprising: determining a maximum likelihood ratio from a series oflikelihood ratios during the time interval for each incoming callcontained in the call data stream; performing a multiple probabilitysimulation and determining a second likelihood ratio for shuffled calldata obtained from the multiple probability simulation; and providing anoutput defining the anomalous call cluster to the emergency managementsystem based on the second likelihood ratio exceeding a threshold. 26.The method of claim 25, further comprising: determining the series oflikelihood ratios for each incoming call in the call data stream usingan expected number of calls during the time interval within thegeographic boundaries defined by a series of circles of increasing radiifrom each incoming call location, from a minimum radius to a maximumradius.
 27. The method of claim 26, further comprising; determining theexpected number of calls within each circle of the series of circlesduring the time interval; and determining the series of likelihoodratios for each incoming call using the determined expected number ofcalls during the time interval.
 28. The method of claim 26, furthercomprising: determining the maximum likelihood ratio by selecting ahighest likelihood ratio from the series of likelihood ratios.
 29. Themethod of claim 26, further comprising: determining the series oflikelihood ratios as a series of Poisson generalized likelihood ratios.30. The method of claim 26, further comprising: obtaining a total numberof calls during the time interval withing each circle of the series ofcircles from the call data stream.
 31. The method of claim 25, furthercomprising: performing the multiple probability simulation by performinga Monte Carlo simulation.
 32. An anomalous call cluster detectorcomprising: a network component, operative to receive emergency data;and a processor, operatively coupled to the network component, theprocessor operative to: detect anomalous call clusters of emergencycalls in a call data stream during a time interval within geographicboundaries; provide a digital map comprising anomalous call clusterinformation for detected anomalous call clusters; and display theanomalous call cluster information on the digital map displayed by anemergency management system.
 33. The anomalous call cluster detector ofclaim 32, wherein the processor is further operative to: provide theanomalous call cluster information comprising a start time and end timefor the time interval, and number of calls within the anomalous callcluster.
 34. The anomalous call cluster detector of claim 32, whereinthe processor is further operative to: determine a maximum likelihoodratio from a series of likelihood ratios during the time interval foreach incoming call contained in the call data stream; perform a multipleprobability simulation and determining a second likelihood ratio forshuffled call data obtained from the multiple probability simulation;and provide an output defining the anomalous call cluster to theemergency management system based on the second likelihood ratioexceeding a threshold.
 35. The anomalous call cluster detector of claim34, wherein the processor is further operative to: determine the seriesof likelihood ratios for each incoming call in the call data streamusing an expected number of calls during a time interval withingeographic boundaries defined by a series of circles of increasing radiifrom each incoming call location, from a minimum radius to a maximumradius.
 36. The anomalous call cluster detector of claim 35, wherein theprocessor is further operative to: determine the expected number ofcalls within each circle of the series of circles during the timeinterval; and determine the series of likelihood ratios for eachincoming call using the determined expected number of calls during thetime interval.
 37. The anomalous call cluster detector of claim 35,wherein the processor is further operative to: determine the maximumlikelihood ratio by selecting a highest likelihood ratio from the seriesof likelihood ratios.
 38. The anomalous call cluster detector of claim35, wherein the processor is further operative to: determine the seriesof likelihood ratios as a series of Poisson generalized likelihoodratios.
 39. The anomalous call cluster detector of claim 35, wherein theprocessor is further operative to: obtain a total number of calls duringthe time interval withing each circle of the series of circles from thecall data stream.
 40. The anomalous call cluster detector of claim 34,wherein the processor is further operative to: perform the multipleprobability simulation by performing a Monte Carlo simulation.